Latest news

Apr. 2012: We have an open PhD position: "Image deblurring under space-variant blur" at the Hubert Curien laboratory and the Observatory of Lyon. Interested students can contact me by e-mail.

Feb. 2012: How to compare noisy patches? This is the question we try to answer in our recent paper published in International Journal of Computer Vision (doi). A preprint version is available on HAL in pdf.

Sept. 2011: The poster I presented at IEEE ICIP in Brussels on shift-variant deblurring is available here pdf.

Sept. 2011: I now have an associate professor position at the University of Saint-Etienne. I teach at TELECOM Saint-Etienne, an Engineering School in electrical engineering. My research focus on image restoration and reconstruction with applications ranging from microscopy and optical metrology to SAR imaging and astronomy.

Apr. 2011: We will present two papers at next ICIP conference in September 2011 in Brussels:

• "Fast model of space-variant blurring and its application to deconvolution in astronomy," together with Eric Thiébaut, and Ferréol Soulez (pdf, abstract Abstract
  Image deblurring is essential to high resolution imaging and is therefore widely used in astronomy, microscopy or computational photography. While shift-invariant blur is modeled by convolution and leads to fast FFT-based algorithms, shift-variant blurring requires models both accurate and fast. When the point spread function (PSF) varies smoothly across the field, these two opposite objectives can be reached by interpolating from a grid of PSF samples. Several models for smoothly varying PSF co-exist in the literature. We advocate that one of them is both physically-grounded and fast. Moreover, we show that the approximation can be largely improved by tuning the PSF samples and interpolation weights with respect to a given continuous model. This improvement comes without increasing the computational cost of the blurring operator. We illustrate the developed blurring model on a deconvolution application in astronomy. Regularized reconstruction with our model leads to large improvements over existing results. ,poster).
• "Patch similarity under non gaussian noise," with Charles Deledalle and Florence Tupin (pdf, abstract Abstract
  Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises. ).

Mar. 2011: Florence Tupin has written a nice article in last issue of IEEE Geoscience and Remote Sensing Newsletter describing recent progress made in image processing of SAR data (link, pdf).

Oct. 2010: Charles Deledalle received the best student paper award at ICIP 2010 in Hong Kong for his fully automatic method to denoise images corrupted by Poisson noise. The method compares noisy patches and pre-filtered patches to define adaptative weights that preserve edges and structures. Charles' denoising method improves on state-of the art denoising techniques based on total variation minimization or wavelets. The paper is available here: "Poisson NL-Means: Unsupervised non-local means for Poisson noise," C. Deledalle, F. Tupin, L. Denis, IEEE International Conference on Image Processing (ICIP), Hong Kong, September 2010 (pdf, abstract Abstract
An extension of the non local (NL) means is proposed for images damaged by Poisson noise. The proposed method is guided by the noisy image and a pre-filtered image and is adapted to the statistics of Poisson noise. The influence of both images can be tuned using two filtering parameters. We propose an automatic setting to select these parameters based on the minimization of the estimated risk (mean square error). This selection uses an estimator of the MSE for NL means with Poisson noise and Newton's method to find the optimal parameters in few iterations. ), and the slides of his presentation are here.

Oct. 2010: At ICIP 2010 in Hong Kong, I presented a paper on image denoising using an image decomposition approach (bounded variations + sparse component). We have shown that exact discrete minimization can be obtained with graph-cuts for TV+L0 decomposition models. The paper is available here: "Exact discrete minimization for TV+L0 image decomposition models," L. Denis, F. Tupin, X. Rondeau, IEEE International Conference on Image Processing (ICIP), Hong Kong, September 2010 (pdf, abstract Abstract
Penalized maximum likelihood denoising approaches seek a solution that fulfills a compromise between data fidelity and agreement with a prior model. Penalization terms are generally chosen to enforce smoothness of the solution and to reject noise. The design of a proper penalization term is a difficult task as it has to capture image variability. Image decomposition into two components of different nature, each given a different penalty, is a way to enrich the modeling. We consider the decomposition of an image into a component with bounded variations and a sparse component. The corresponding penalization is the sum of the total variation of the first component and the L0 pseudo-norm of the second component. The minimization problem is highly non-convex, but can still be globally minimized by a minimum s-t-cut computation on a graph. The decomposition model is applied to synthetic aperture radar image denoising. ), and the slides here.

Aug. 2010: Our study on resolution in holography based on Cramér-Rao lower bounds ("On the single point resolution of on-axis digital holography," C. Fournier, L. Denis, and T. Fournel, J. Opt. Soc. Am. A, 27 (8), 1856-1862, 2010: pdf, doi, abstract Abstract
On-axis digital holography (DH) is becoming widely used for its time-resolved three-dimensional (3D) imaging capabilities. A 3D volume can be reconstructed from a single hologram. DH is applied as a metrological tool in experimental mechanics, biology, and fluid dynamics, and therefore the estimation and the improvement of the resolution are current challenges. However, the resolution depends on experimental parameters such as the recording distance, the sensor definition, the pixel size, and also on the location of the object in the field of view. This paper derives resolution bounds in DH by using estimation theory. The single point resolution expresses the standard deviations on the estimation of the spatial coordinates of a point source from its hologram. Cramér Rao lower bounds give a lower limit for the resolution. The closed-form expressions of the Cramér Rao lower bounds are obtained for a point source located on and out of the optical axis. The influences of the 3D location of the source, the numerical aperture, and the signal-to-noise ratio are studied. ) is featured in Spotlight on Optics.

General information

I have an associate professor position at the University of Saint-Etienne. I teach at TELECOM Saint-Etienne, an Engineering School in electrical engineering. I conduct my research activity at the Laboratoire Hubert Curien. My main interest is on image restoration and reconstruction with applications ranging from microscopy and optical metrology to SAR imaging and astronomy.

Previous positions:

In 2010-2011, I have spent a year and a half as a research scientist at the Observatory of Lyon on inverse problems in astronomy and biomedical imaging. My position was funded by the French Research Agency (research project "MITIV" lead by Eric Thiébaut).

From 2007 to the end of 2009, I was Assistant Professor at the Electrical Engineering Department of the Engineering School 'CPE Lyon'. I used to teach image processing, computer graphics and computer science. My research focused on image reconstruction/restoration problems that occur in imaging, especially in synthetic aperture radar and digital holography.

In 2006-2007, I worked for one year at Télécom Paristech as a postdoctoral scholar at the Image Processing Team of the Signal and Image Processing Department. My work was on synthetic aperture radar (SAR) images and optical images to design algorithms for automatic extraction of elevation information in urban areas. I focused on radar image denoising with graph-cut. SAR image denoising is a research subject on which I am still working.

Digital holography was the main subject of my PhD thesis, defended in autumn 2006 in Saint-Etienne University (France).

Research

Research projects

Grants

Co-workers

I have the pleasure to work with the following colleagues (non-exhaustive list!):

• Jean-François Aujol, CNRS Associate scientist at ENS Cachan
• Jérôme Darbon, CNRS Associate scientist at ENS Cachan
• Charles Deledalle, PhD candidate at Telecom Paristech
• Christophe Ducottet, Professor at Université de St-Etienne
• Thierry Fournel, Professor at Université de St-Etienne
• Corinne Fournier, Assistant Professor at Telecom St-Etienne
• Charles Goepfert, PhD candidate at Ecole Centrale de Lyon
• Jérôme Gire, teaching associate at Université de St-Etienne
• Dominique Jeulin, Professor at Ecole des Mines de Paris
• Dirk Lorenz, Assistant Professor at TU Braunschweig
• Xavier Rondeau, Post-doctorate fellow at Telecom Paristech
• Marc Sigelle, Assistant Professor at Telecom Paristech
• Ferréol Soulez, Post-doctorate fellow at the Observatory of Lyon
• Eric Thiébaut, Astronomer at the Observatory of Lyon
• Céline Tison, Research Engineer at CNES
• Dennis Trede, PhD candidate at Bremen Universität
• Florence Tupin, Professor at Telecom Paristech

Publications

Papers in refereed journals

2012

[15] "How to Compare Noisy Patches? Patch Similarity Beyond Gaussian Noise," C. Deledalle, L. Denis, and F. Tupin, International Journal of Computer Vision, 2012 (pdf, doi, abstract Abstract
Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches (so-called image-based) compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. Recent progress in natural image modeling also makes intensive use of patch comparison.
A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared differences of intensities. For the case where noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature of image processing, detection theory and machine learning.
By expressing patch (dis)similarity as a detection test under a given noise model, we introduce these criteria with a new one and discuss their properties. We then assess their performance for different tasks: patch discrimination, image denoising, stereo-matching and motion-tracking under gamma and Poisson noises. The proposed criterion based on the generalized likelihood ratio is shown to be both easy to derive and powerful in these diverse applications. ).
 
[14] "Testing an in-line digital holography 'inverse method' for the Lagrangian tracking of evaporating droplets in homogeneous nearly isotropic turbulence," D. Chareyron, J.L. Marié, C. Fournier, J. Gire, N. Grosjean, L. Denis, M. Lance and L. Méès, New Journal of Physics, 14 043039, 2012 (doi, pdf, HAL, abstract Abstract
An in-line digital holography technique is tested, the objective being to measure Lagrangian three-dimensional (3D) trajectories and the size evolution of droplets evaporating in high-Re strong turbulence. The experiment is performed in homogeneous, nearly isotropic turbulence (50 × 50 × 50 mm3) created by the meeting of six synthetic jets. The holograms of droplets are recorded with a single high-speed camera at frame rates of 1-3 kHz. While hologram time series are generally processed using a classical approach based on the Fresnel transform, we follow an 'inverse problem' approach leading to improved size and 3D position accuracy and both in-field and out-of-field detection. The reconstruction method is validated with 60 microns diameter water droplets released from a piezoelectric injector 'on-demand' and which do not appreciably evaporate in the sample volume. Lagrangian statistics on 1000 reconstructed tracks are presented. Although improved, uncertainty on the depth positions remains higher, as expected in in-line digital holography. An additional filter is used to reduce the effect of this uncertainty when calculating the droplet velocities and accelerations along this direction. The diameters measured along the trajectories remain constant within ±1.6%, thus indicating that accuracy on size is high enough for evaporation studies. The method is then tested with R114 freon droplets at an early stage of evaporation. The striking feature is the presence on each hologram of a thermal wake image, aligned with the relative velocity fluctuations 'seen' by the droplets (visualization of the Lagrangian fluid motion about the droplet). Its orientation compares rather well with that calculated by using a dynamical equation for describing the droplet motion. A decrease of size due to evaporation is measured for the droplet that remains longest in the turbulence domain. ).
 

2011

[13] "NL-InSAR: Non-local interferogram estimation," C. Deledalle, L. Denis, and F. Tupin, IEEE trans. geoscience and remote sensing, 49, 4, 2011 (pdf, doi, abstract Abstract
Interferometric synthetic aperture radar (InSAR) data provides reflectivity, phase difference and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3D reconstruction. These images are estimated in practice from hermitian product on local windows. These windows lead to biases and resolution losses due to local heterogeneity caused by edges and texture. This paper proposes a non-local approach for joint estimation of the reflectivity, phase difference and coherence images from an interferometric pair of co-registered single-look complex (SLC) SAR images. Non-local techniques are known to efficiently reduce noise while preserving structures by performing a weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are 'resembling'. Patch-similarity is usually defined as the Euclidean distance between the vectors of graylevels. In this paper a statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from intensity and phase difference, and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate. The efficiency of this new interferogram construction technique is illustrated both qualitatively and quantitatively on synthetic and true data. ).
 

2010

[12] "On the single point resolution of on-axis digital holography," C. Fournier, L. Denis, and T. Fournel, J. Opt. Soc. Am. A, 27 (8), 1856-1862, 2010. (pdf, doi, abstract Abstract
On-axis digital holography (DH) is becoming widely used for its time-resolved three-dimensional (3D) imaging capabilities. A 3D volume can be reconstructed from a single hologram. DH is applied as a metrological tool in experimental mechanics, biology, and fluid dynamics, and therefore the estimation and the improvement of the resolution are current challenges. However, the resolution depends on experimental parameters such as the recording distance, the sensor definition, the pixel size, and also on the location of the object in the field of view. This paper derives resolution bounds in DH by using estimation theory. The single point resolution expresses the standard deviations on the estimation of the spatial coordinates of a point source from its hologram. Cramér Rao lower bounds give a lower limit for the resolution. The closed-form expressions of the Cramér Rao lower bounds are obtained for a point source located on and out of the optical axis. The influences of the 3D location of the source, the numerical aperture, and the signal-to-noise ratio are studied. ).
 

2009

[11] "Inline hologram reconstruction with sparsity constraints," L. Denis, D. Lorenz, E. Thiébaut, C. Fournier, D. Trede, Optics Letters, 34(22), 3475-3477, 2009. (pdf, doi, abstract Abstract
Inline digital holograms are classically reconstructed using linear operators to model diffraction. It has long been recognized that such reconstruction operators do not invert the hologram formation operator. Classical linear reconstructions yield images with artifacts such as distortions near the field-of-view boundaries or twin images. When objects located at different depths are reconstructed from a hologram, in-focus and out-of-focus images of all objects superimpose upon each other. Additional processing, such as maximum-of-focus detection, is thus unavoidable for any successful use of the reconstructed volume. In this Letter, we consider inverting the hologram formation model in a Bayesian framework. We suggest the use of a sparsity-promoting prior, verified in many inline holography applications, and present a simple iterative algorithm for 3D object reconstruction under sparsity and positivity constraints. Preliminary results with both simulated and experimental holograms are highly promising. ).
 
[10] "Greedy Solution of Ill-Posed Problems: Error Bounds and Exact Inversion," L. Denis, D. Lorenz and D. Trede, Inverse Problems, 25 115017, 2009. (pdf,doi, abstract Abstract
The orthogonal matching pursuit (OMP) is a greedy algorithm to solve sparse approximation problems. Sufficient conditions for exact recovery are known with and without noise. In this paper we investigate the applicability of the OMP for the solution of ill-posed inverse problems in general, and in particular for two deconvolution examples from mass spectrometry and digital holography, respectively. In sparse approximation problems one often has to deal with the problem of redundancy of a dictionary, i.e. the atoms are not linearly independent. However, one expects them to be approximatively orthogonal and this is quantified by the so-called incoherence. This idea cannot be transferred to ill-posed inverse problems since here the atoms are typically far from orthogonal. The ill-posedness of the operator probably causes the correlation of two distinct atoms to become huge, i.e. that two atoms look much alike. Therefore, one needs conditions which take the structure of the problem into account and work without the concept of coherence. In this paper we develop results for the exact recovery of the support of noisy signals. In the two examples, mass spectrometry and digital holography, we show that our results lead to practically relevant estimates such that one may check a priori if the experimental setup guarantees exact deconvolution with OMP. Especially in the example from digital holography, our analysis may be regarded as a first step to calculate the resolution power of droplet holography. ) -- Note that the authors of this paper are ordered alphabetically, the main author is D. Trede.
 
[9] "Iterative weighted maximum likelihood denoising with probabilistic patch-based weights," C. Deledalle, L. Denis, and F. Tupin, IEEE trans. image processing, 18, 12, 2009. (pdf, doi, abstract Abstract
Image denoising is an important problem in image processing since noise may interfere with visual or automatic interpretation. This paper presents a new approach for image denoising in the case of a known uncorrelated noise model. The proposed filter is an extension of the nonlocal means (NL means) algorithm introduced by Buades et al., which performs a weighted average of the values of similar pixels. Pixel similarity is defined in NL means as the Euclidean distance between patches (rectangular windows centered on each two pixels). In this paper, a more general and statistically grounded similarity criterion is proposed which depends on the noise distribution model. The denoising process is expressed as a weighted maximum likelihood estimation problem where the weights are derived in a data-driven way. These weights can be iteratively refined based on both the similarity between noisy patches and the similarity of patches extracted from the previous estimate. We show that this iterative process noticeably improves the denoising performance, especially in the case of low signal-to-noise ratio images such as synthetic aperture radar (SAR) images. Numerical experiments illustrate that the technique can be successfully applied to the classical case of additive Gaussian noise but also to cases such as multiplicative speckle noise. The proposed denoising technique seems to improve on the state of the art performance in that latter case. ).
 
[8] "Joint regularization of phase and amplitude of InSAR data: application to 3D reconstruction," L. Denis, F. Tupin, J. Darbon, and M. Sigelle, IEEE trans. geoscience and remote sensing, 47, 11, 2009. (pdf, doi, abstract Abstract
Interferometric synthetic aperture radar (SAR) images suffer from a strong noise, and their regularization is often a prerequisite for successful use of their information. Independently of the unwrapping problem, interferometric phase denoising is a difficult task due to shadows and discontinuities. In this paper, we propose to jointly filter phase and amplitude data in a Markovian framework. The regularization term is expressed by the minimization of the total variation and may combine different information (phase, amplitude, optical data). First, a fast and approximate optimization algorithm for vectorial data is briefly presented. Then, two applications are described. The first one is a direct application of this algorithm for 3-D reconstruction in urban areas with very high resolution images. The second one is an adaptation of this framework to the fusion of SAR and optical data. Results on aerial SAR images are presented. ).
 
[7] "SAR Image Regularization with Fast Approximate Discrete Minimization," L. Denis, F. Tupin, J. Darbon, and M. Sigelle, IEEE trans. image processing, 18, 7, 2009. (pdf, doi, abstract Abstract
Synthetic aperture radar (SAR) images, like other coherent imaging modalities, suffer from speckle noise. The presence of this noise makes the automatic interpretation of images a challenging task and noise reduction is often a prerequisite for successful use of classical image processing algorithms. Numerous approaches have been proposed to filter speckle noise. Markov random field (MRF) modelization provides a convenient way to express both data fidelity constraints and desirable properties of the filtered image. In this context, total variation minimization has been extensively used to constrain the oscillations in the regularized image while preserving its edges. Speckle noise follows heavy-tailed distributions, and the MRF formulation leads to a minimization problem involving nonconvex log-likelihood terms. Such a minimization can be performed efficiently by computing minimum cuts on weighted graphs. Due to memory constraints, exact minimization, although theoretically possible, is not achievable on large images required by remote sensing applications. The computational burden of the state-of-the-art algorithm for approximate minimization (namely the alpha -expansion) is too heavy specially when considering joint regularization of several images. We show that a satisfying solution can be reached, in few iterations, by performing a graph-cut-based combinatorial exploration of large trial moves. This algorithm is applied to joint regularization of the amplitude and interferometric phase in urban area SAR images. ).
 

2008

[6] "Digital holography of particles: benefits of the 'inverse problem' approach," J. Gire, L. Denis, C. Fournier, C. Ducottet, E. Thiebaut, and F. Soulez, Meas. Sci. Tech., 19, 2008. (pdf, doi, abstract Abstract
The potential of in-line digital holography to locate and measure the size of particles distributed throughout a volume (in one shot) has been established. These measurements are fundamental for the study of particle trajectories in fluid flow. The most important issues in digital holography today are poor depth positioning accuracy, transverse field-of-view limitations, border artifacts and computational burdens. We recently suggested an 'inverse problem' approach to address some of these issues for the processing of particle digital holograms. The described algorithm improves axial positioning accuracy, gives particle diameters with sub-micrometer accuracy, eliminates border effects and increases the size of the studied volume. This approach for processing particle holograms pushes back some classical constraints. For example, the Nyquist criterion is no longer a restriction for the recording step and the studied volume is no longer confined to the field of view delimited by the sensor borders. In this paper we present a review of the limitations commonly found in digital holography. We then discuss the benefits of the 'inverse problem' approach and the influence of some experimental parameters in this framework. ).
 
[5] "Numerical suppression of the twin-image in in-line holography of a volume of micro-objects," L. Denis, C. Fournier, T. Fournel, and C. Ducottet, Meas. Sci. Tech., 19, 2008. (pdf, doi, abstract Abstract
We address the twin-image problem that arises in holography due to the lack of phase information in intensity measurements. This problem is of great importance in in-line holography where spatial elimination of the twin image cannot be carried out as in off-axis holography. A unifying description of existing digital suppression methods is given in the light of deconvolution techniques. Holograms of objects spread in 3D cannot be processed through available approaches. We suggest an iterative algorithm and demonstrate its efficacy on both simulated and real data. This method is suitable to enhance the reconstructed images from a digital hologram of small objects. ).
 

2007

[4] "Inverse problem approach for particle digital holography: out-of-field particle detection made possible," F. Soulez, L. Denis, E. Thiébaut, C. Fournier, and C. Goepfert, J. Opt. Soc. Am. A, 24 (12), 3708-3716, 2007. (pdf, doi, abstract Abstract
We propose a microparticle detection scheme in digital holography. In our inverse problem approach, we estimate the optimal particles set that best models the observed hologram image. Such a method can deal with data that have missing pixels. By considering the camera as a truncated version of a wider sensor, it becomes possible to detect particles even out of the camera field of view. We tested the performance of our algorithm against simulated and experimental data for diluted particle conditions. With real data, our algorithm can detect particles far from the detector edges in a working area as large as 16 times the camera field of view. A study based on simulated data shows that, compared with classical methods, our algorithm greatly improves the precision of the estimated particle positions and radii. This precision does not depend on the particle's size or location (i.e., whether inside or outside the detector field of view). ).
 
[3] "Inverse problem approach for particle digital holography: accurate location based on local optimisation," F. Soulez, L. Denis, C. Fournier, E. Thiébaut, and C. Goepfert, J. Opt. Soc. Am. A, 24 (4), 1164-1171, 2007. (pdf, doi, abstract Abstract
We propose a microparticle localization scheme in digital holography. Most conventional digital holography methods are based on Fresnel transform and present several problems such as twin-image noise, border effects, and other effects. To avoid these difficulties, we propose an inverse-problem approach, which yields the optimal particle set that best models the observed hologram image. We resolve this global optimization problem by conventional particle detection followed by a local refinement for each particle. Results for both simulated and real digital holograms show strong improvement in the localization of the particles, particularly along the depth dimension. In our simulations, the position precision is >1 micron rms. Our results also show that the localization precision does not deteriorate for particles near the edge of the field of view. ).
 
[2] "Reconstruction of the rose of directions from a digital micro-hologram of fibers," L. Denis, T. Fournel, C. Fournier, and D. Jeulin, J. Microsc., 225 (3), 282-291, 2007. (pdf, doi, abstract Abstract
Digital holography makes it possible to acquire quickly the interference patterns of objects spread in a volume. The digital processing of the fringes is still too slow to achieve on line analysis of the holograms. We describe a new approach to obtain information on the direction of illuminated objects. The key idea is to avoid reconstruction of the volume followed by classical three-dimensional image processing. The hologram is processed using a global analysis based on autocorrelation. A fundamental property of diffraction patterns leads to an estimate of the mean geometric covariogram of the objects projections. The rose of directions is connected with the mean geometric covariogram through an inverse problem. In the general case, only the two-dimensional rose of the object projections can be reconstructed. The further assumption of unique-size objects gives access with the knowledge of this size to the three-dimensional direction information. An iterative scheme is suggested to reconstruct the three-dimensional rose in this special case. Results are provided on holograms of paper fibres. ).
 

2006

[1] "Direct extraction of mean particle size from a digital hologram," L. Denis, C. Fournier, T. Fournel, C. Ducottet, and D. Jeulin, Applied Optics, 45 (5), 944-952, 2006. (pdf, doi, abstract Abstract
Digital holography, which consists of both acquiring the hologram image in a digital camera and numerically reconstructing the information, offers new and faster ways to make the most of a hologram. We describe a new method to determine the rough size of particles in an in-line hologram. This method relies on a property that is specific to interference patterns in Fresnel holograms: Self-correlation of a hologram provides access to size information. The proposed method is both simple and fast and gives results with acceptable precision. It suppresses all the problems related to the numerical depth of focus when large depth volumes are analyzed. ).
 

Conference papers

2012

[24] "Blind deconvolution of 3D data in wide field fluorescence microscopy," F. Soulez, L. Denis, Y. Tourneur, and E. Thiébaut, IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, April 2012 (pdf, HAL, abstract Abstract
In this paper we propose a blind deconvolution algorithm for wide field fluorescence microscopy. The 3D PSF is modeled after a parametrized pupil function. The PSF parameters are estimated jointly with the object in a maximum a posteriori framework. We illustrate the performances of our algorithm on experimental data and show significant resolution improvement notably along the depth. Quantitative measurements on images of calibration beads demonstrate the benefits of blind deconvolution both in terms of contrast and resolution compared to non-blind deconvolution using a theoretical PSF. ).
 

2011

[23] "Fast model of space-variant blurring and its application to deconvolution in astronomy," L. Denis, E. Thiébaut, and F. Soulez, IEEE International Conference on Image Processing (ICIP), Brussels, September 2011 (pdf, abstract Abstract
Image deblurring is essential to high resolution imaging and is therefore widely used in astronomy, microscopy or computational photography. While shift-invariant blur is modeled by convolution and leads to fast FFT-based algorithms, shift-variant blurring requires models both accurate and fast. When the point spread function (PSF) varies smoothly across the field, these two opposite objectives can be reached by interpolating from a grid of PSF samples. Several models for smoothly varying PSF co-exist in the literature. We advocate that one of them is both physically-grounded and fast. Moreover, we show that the approximation can be largely improved by tuning the PSF samples and interpolation weights with respect to a given continuous model. This improvement comes without increasing the computational cost of the blurring operator. We illustrate the developed blurring model on a deconvolution application in astronomy. Regularized reconstruction with our model leads to large improvements over existing results. ,poster).
 
[22] "Patch similarity under non gaussian noise," C. Deledalle, F. Tupin, and L. Denis, IEEE International Conference on Image Processing (ICIP), Brussels, September 2011 (pdf, abstract Abstract
Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from "real" data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises. ).
 
[21] "Influence of speckle filtering of polarimetric SAR data on different classification methods," F. Cao, C. Deledalle, J.-M. Nicolas, F. Tupin, L. Denis, L. Ferro-Famil, E. Pottier, and C. Lopez-Martinez, IEEE International Geoscience and Remote Sensing Symposium, Vancouver, July 2011.
 
[20] "Inverse problem approach for digital hologram reconstruction," C. Fournier, L. Denis, E. Thiébaut, T. Fournel and M. Seifi, SPIE 3D Imaging, Visualization and Display, Orlando, April 2011 (pdf, abstract Abstract
Digital holography (DH) is being increasingly used for its time-resolved three-dimensional (3-D) imaging capabilities. A 3-D volume can be numerically reconstructed from a single 2-D hologram. Applications of DH range from experimental mechanics, biology, and fluid dynamics. Improvement and characterization of the 3-D reconstruction algorithms is a current issue. Over the past decade, numerous algorithms for the analysis of holograms have been proposed. They are mostly based on a common approach to hologram processing: digital reconstruction based on the simulation of hologram diffraction. They suffer from artifacts intrinsic to holography: twin-image contamination of the reconstructed images, image distortions for objects located close to the hologram borders. The analysis of the reconstructed planes is therefore limited by these defects. In contrast to this approach, the inverse problems perspective does not transform the hologram but performs object detection and location by matching a model of the hologram. Information is thus extracted from the hologram in an optimal way, leading to two essential results: an improvement of the axial accuracy and the capability to extend the reconstructed field beyond the physical limit of the sensor size (out-of-field reconstruction). These improvements come at the cost of an increase of the computational load compared to (typically non iterative) classical approaches. ).
 

2010

[19] "Exact discrete minimization for TV+L0 image decomposition models," L. Denis, F. Tupin, X. Rondeau, IEEE International Conference on Image Processing (ICIP), Hong Kong, September 2010 (pdf, abstract Abstract
Penalized maximum likelihood denoising approaches seek a solution that fulfills a compromise between data fidelity and agreement with a prior model. Penalization terms are generally chosen to enforce smoothness of the solution and to reject noise. The design of a proper penalization term is a difficult task as it has to capture image variability. Image decomposition into two components of different nature, each given a different penalty, is a way to enrich the modeling. We consider the decomposition of an image into a component with bounded variations and a sparse component. The corresponding penalization is the sum of the total variation of the first component and the L0 pseudo-norm of the second component. The minimization problem is highly non-convex, but can still be globally minimized by a minimum s-t-cut computation on a graph. The decomposition model is applied to synthetic aperture radar image denoising. , slides).
 
[18] "Poisson NL-Means: Unsupervised non-local means for Poisson noise," C. Deledalle, F. Tupin, L. Denis, IEEE International Conference on Image Processing (ICIP), Hong Kong, September 2010 (pdf, abstract Abstract
An extension of the non local (NL) means is proposed for images damaged by Poisson noise. The proposed method is guided by the noisy image and a pre-filtered image and is adapted to the statistics of Poisson noise. The influence of both images can be tuned using two filtering parameters. We propose an automatic setting to select these parameters based on the minimization of the estimated risk (mean square error). This selection uses an estimator of the MSE for NL means with Poisson noise and Newton's method to find the optimal parameters in few iterations. , slides).
 
[17] "Polarimetric SAR estimation based on non-local means," C. Deledalle, F. Tupin, L. Denis, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, July 2010 ( abstract Abstract
Recently, non-local approaches have been proved relevant for image restoration. Unlike local filters, the non-local (NL) means decrease the noise while preserving well the resolution. In the proposed paper, we suggest the use of a non-local approach to estimate single-look SAR reflectivity images or to construct SAR interferograms. SAR interferogram construction refers to the joint estimation of the reflectivity, phase difference and coherence image froma pair of two co-registered single-look complex SAR images. This paper is composed of four sections. Section 2 recalls the non-local (NL) means. Weighted maximum likelihood is then introduced in Section 3 as a generalization of the weighted average performed in the NL means. In Section 4, we propose to set the weights according to the probability of similarity which provides an extension of the Euclidean distance used in the NL means. Finally, experiments and results are presented in Section 5 to show the efficiency of the proposed approach. , slides).
 
[16] "A non-local approach for SAR and interferometric SAR denoising," C. Deledalle, F. Tupin, L. Denis, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, July 2010 ( abstract Abstract
During the past few years, the non-local (NL) means have proved their efficiency for image denoising. This approach assumes there exist enough redundant patterns in images to be used for noise reduction. We suggest that the same assumption can be done for polarimetric synthetic aperture radar (PolSAR) images. In its original version, theNLmeans dealwith additivewhite Gaussian noise, but several extensions have been proposed for non-Gaussian noise. This paper applies the methodology proposed in [9] to PolSAR data. The proposed filter seems to deal well with the statistical properties of speckle noise and themulti-dimensional nature of such data. Results are given on synthetic and L-Band E-SAR data to validate the proposed method. , slides).
 
[15] "Glacier monitoring: correlation versus texture tracking," C. Deledalle, J.M. Nicolas, F. Tupin, L. Denis, R. Fallourd, E. Trouvé, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, July 2010.
 
[14] "A Comparative Review of SAR Images Speckle Denoising Methods Based on Functional Minimization," J-F Aujol, E. Bratsolis, J. Darbon, L. Denis, J-M. Nicolas, X. Rondeau, M. Sigelle and F. Tupin, SIAM Conference on Imaging Science, Chicago, 12-14th april 2010 -- This work has been presented by Marc Sigelle.
 

2009

[13] "Resolution in in-line digital holography," C. Fournier, L. Denis, T. Fournel, Workshop on Information Optics, J. Phys.: Conf. Ser., 206, 012025, Paris, France, July 2009 (doi).
 
[12] "Lagrangian measurement of droplet in homogeneous isotropic turbulence by digital in-line holography", D Chareyron, J-L Marié, M. Lance, J. Gire, C. Fournier, L. Denis, 11th International Symposium on Gas-Liquid Two-Phase Flows, FEDSM2009, Vail (Colorado) 2-5 August 2009.
 
[11] "Digital holography measurements of Lagrangian trajectories and diameters of droplets in an isotropic turbulence," D. Chareyron, J.L. Marié, M. Lance, J. Gire, C. Fournier, L. Denis, 6th International Symposium on Multiphase Flow, Heat Mass Transfert and Energy Conversion, Xi'an (China) 11-15 July 2009.
 

2008

[10] "Joint filtering of SAR interferometric phase and amplitude data in urban areas by TV minimization," L. Denis, L, F Tupin, Darbon, et Sigelle, IEEE International Geoscience and Remote Sensing Symposium, Boston, 2008. (pdf, doi)
 
[9] "A regularization approach for InSAR and optical data fusion," L. Denis, Tupin, Darbon, et Sigelle, IEEE International Geoscience and Remote Sensing Symposium, Boston, 2008. (pdf, doi)
 
[8] "SAR amplitude filtering using TV prior and its application to building delineation," L. Denis, Tupin, Darbon, Sigelle, et Tison, 7th European Conference on Synthetic Aperture Radar, Friedrichshafen, Germany, 2008. (pdf, doi)
 
[7] "Signal to noise characterization of an inverse problem-based algorithm for digital inline holography," J. Gire, C. Ducottet, L. Denis, E. Thiebaut, F. Soulez, Proceedings of the International Symposium on Flow Visualization, (CDROM), S39:ID226. Nice: JP Prenel - Y Bailly, 2008. (pdf)
 

2007

[6] "Inverse problem approach for Digital Holographic Particle Tracking: Influence of the experimental parameters and benefits," C. Fournier, J. Gire, L. Denis, E. Thiebaut, F. Soulez, and C. Ducottet, Workshop on Digital Holographic Reconstruction and Tomography, Loughborough, England, April 2007.
 
[5] "Inverse Problem Approach for Particle Digital Holography: Field of View Extrapolation and Accurate Location," F. Soulez, E. Thiébaut, L. Denis, and C. Fournier, Adaptive Optics: Analysis and Methods / Computational Optical Sensing and Imaging / Information Photonics / Signal Recovery and Synthesis Topical Meetings, Vancouver, Canada, June 2007. (doi)
 
[4] "Inverse problem approach for particle digital holography: particle detection and accurate location," F. Soulez, L. Denis, C. Fournier, E. Thiébaut, and C. Goepfert, Proceedings of the Physics in Signal and Image Processing, Mulhouse, France, January 2007. (pdf)
 

2006

[3] "Digital Holography compared to Phase Doppler Anemometry: study of an experimental droplet flow," C. Fournier, C. Goepfert, J. L. Marié, L. Denis, F. Soulez, M. Lance, et J. P. Schon, Proceedings of the 12th International Symposium on Flow Visualization, (ed. Optimage Ltd), ISBN : 0-9533991-8-4,19.4, p228, Göttingen, Germany, September 2006.
 
[2] "Cleaning digital holograms to investigate 3D particle fields," L. Denis, T. Fournel, C. Fournier, et C. Ducottet, Proceedings of the 12th International Symposium on Flow Visualization, (ed. Optimage Ltd),ISBN : 0-9533991-8-4, 69.4, p215, Göttingen, Germany, September 2006.
 

2005

[1] "Twin-image noise reduction by phase retrieval in in-line digital holography," L. Denis, C. Fournier, T. Fournel, C. Ducottet, Wavelets XI, SPIE's Symposium on Optical Science and Technology, vol 5914, pp 59140J, San Diego, CA, USA, 2005. (pdf, doi)
 

Teaching

I used to teach approximately 200 hours per year when I was assistant professor at CPE Lyon. My students were attending the Engineering School CPE Lyon in the Electrical Engineering department. I covered the following topics, together with other colleagues:

Image processing (lectures + lab sessions)

• Image filtering
• Image segmentation
• Statistical pattern recognition
• Remote sensing
• Markov random fields and graph-cuts

Computer graphics (lectures + lab sessions)

• Raytracing
• Computational geometry
• Rendering
• OpenGL introduction

Unix systems programming (lab sessions)

• File system
• Process creation
• Inter-process communication
• Threads

Signals and Linear Systems (tutorials + lab sessions)

• Spectral representation
• Sampling
• Linear filtering
• Modulation

Misc

How to create a bibliographic style for LaTeX/BibTeX

Scientific journals have strictly defined bibliographic conventions for typesetting references. Unfortunately for LaTeX users, these journals do not always provide a bibliogaphic style (i.e. a .bst file). This page describes how to create one yourself and gives one such file I created for use with Journal of Microscopy.
 

Using makebst

A very usefull tool for creating BibTeX styles is makebst. It's use is extremly simple: you just have to run LaTeX: latex makebst and answer a bunch of questions. An output file of type .bst will be created for use as any other bibliographic style.

Here is an extract of the questions you have to answer:

The new style mystyle.bst can then be used in your LaTeX file to typset the bibliographical entries stored in your BibTeX file mybib.bib with the following two lines of code:

Unofficial bibliographic style for Journal of Microscopy

I have created with the previously described procedure a .bst file for a paper I have written and published in Journal of Microscopy. I tried to answer the best as I could to the questions of makebst script, but I cannot guarantee that the file I generated fullfill all requirements of the journal. The file can be downloaded here. Please contact me if you see any improvement to be done on the file or if you want me to display a link to your own .bst file.

In addition to using the provided .bst file, you will have to include natbib package. This package provides variants of LaTeX \cite command. The command \citep adheres to Journal of Microscopy's citing convention.

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Last update: February 2012