Contact - General information - Research - Publications - Teaching - Misc
Loïc Denis
Loïc Denis
Centre de Recherche Astrophysique de Lyon
UMR 5574 CNRS / Université Lyon 1 / ENS de Lyon
Observatoire de Lyon
9, avenue Charles André
F-69561 Saint Genis Laval cedex
France

(+33) 4 78 86 83 94
 
loic dot denis at cpe dot fr

General information

Since early 2010 I am working at the Observatory of Lyon on inverse problems in astronomy and biomedical imaging. My position is funded by the French Research Agency (research project "MITIV" lead by Eric Thiébaut).

Before joining the Observatory, I worked for one year (2006-2007) 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.

From 2007 to the end of 2009, I also hold an assistant professor position 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.

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

Research

Research projects

Non-local denoising of synthetic aperture radar images

2008 - present


Context

denoising result Image denoising is a fundamental low-level task in many applications. Numerous denoising techniques have been proposed, but only few of them provide a general methodology that apply to different noise models (e.g., additive, multiplicative). This project is concerned with the development of non-local denoising techniques adapted to given noise distributions.

Key idea(s)

Simple denoising techniques replace the noisy values with the Maximum Likelihood (ML) estimate computed over a small neighboring window. They lead to a loss of resolution, i.e., edges are blurred, as the window shape is unchanged even over homogeneous region boundaries where it overlaps pixels with very different values. A straightforward improvement is then to adapt spatially the window shape based on the homogeneity inside the window. A more powerful approach, deriving from the NL-means introduced by Buades et al., is to consider weighted maximum likelihood. Weights are set in a data-driven fashion based on the similarity between image patches.

Results

We suggest a general methodology to define the similarity between noisy patches as well as between restored patches. This leads to an iterative algorithm which gives good results on images corrupted with additive Gaussian noise, and outperforms state-of-the art denoising techniques for images with speckle noise such as synthetic aperture radar (SAR) images.

More information on Charles Deledalle's webpage.

Related publications:

"NL-InSAR: Non-local interferogram estimation," C. Deledalle, L. Denis, and F. Tupin, Technical report, oct. 2009 (pdf).
 
"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)
 

Joint regularization with graph-cuts

2007 - present


Context

graph Graph-cuts are powerful techniques that can be used to solve combinatorial minimization problems in processing. In the context of image regularization, they can find the global minimum of first-order Markov Random Field energies (i.e., energies involving only single and pair-wise terms) with convex regularization. This discrete minimization is performed by computing a minimum-cost cut over a huge graph. The size of the graph prevents from using directly such techniques on large images (million pixels images). Joint regularization cannot be handled with such graph constructs. Combinatorial approaches are however desirable to minimize energies with non-convex neg log-likelihood such as with SAR imaging.

Key idea(s)

Approximate minimization can be performed by considering a sequence of sub-problems. Each of these sub-problems can be exactly solved using two-levels graphs.

Results

A fast algorithm for approximate discrete minimization is proposed. It is suitable for minimization of scalar or vectorial fields with convex prior. The technique is applied to joint regularization of amplitude and phase of interferometric SAR images, and to 3D reconstruction from an interferometric SAR pair and an optical image.

Related publications:

"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)
 
"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)
 

Sparse reconstruction in digital holography

2008 - present


Context

sparse reconstruction 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.

Key idea(s)

We consider inverting the hologram formation model in Bayesian framework. We suggest the use of a sparsity-promoting prior, intrinsically verified due to inline holography requirements, and present a simple iterative algorithm for 3D object reconstruction under sparsity and positivity constraints.

Results

Out-of-focus images of objects are strongly attenuated or absent in reconstructed 3D images. The sparse reconstruction technique makes it possible to reconstruct outside of the field of view.
It is also possible to extend the recent theoretical results on conditions for exact recovery of sparse signals with orthogonal matching pursuit to the case of noisy data. In the case of digital holography of particles, this gives upper bounds on the achievable resolution.

Related publications:

"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)
 
"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) -- Note that the authors of this paper are ordered alphabetically, the main author is D. Trede.
 

Particle detection in digital holography

2006 - 2008


Context

particle detection algorithm Digital holography is the method of choice for time-resolved 3D measurement of the location of particles in a flow. These measurements are crucial to validate numerical simulations of turbulence. The 3D location of several particles can be recovered from a single hologram by analyzing their diffraction patterns. Classically, this is performed in two steps: first, a 3D volume is reconstructed by simulating optical diffraction of the hologram. Then, the maximum of focus location of the image of each particle is detected. These approaches suffer from severe bias close to the hologram boundaries, and false detections occur due to multiple focusing or speckle noise.

Key idea(s)

Such drawbacks can be circumvent by following an inverse problem approach. Instead of reconstructing a 3D volume image from the hologram, particles are directly detected by matching the diffraction patterns on the hologram. An approach similar to the matching pursuit algorithm is proposed, with sub-pixel refinement by local optimization.

Results

The accuracy of the detection is improved by a factor 5 compared to that of classical techniques in a standard experimental configuration. Out-of-field detection is demonstrated, even far from the hologram boundaries.

Related publications:

"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)
 
"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)
 
"Digital holography of particles: experimental parameters setting and benefits of the inverse problems approach," J. Gire, L. Denis, C. Fournier, C. Ducottet, E. Thiebaut, and F. Soulez, Meas. Sci. Tech., 19, 2008. (pdf, doi)
 
"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)
 

Extraction of size/orientation information from a hologram

2005 - 2007


Context

autocovariance Digital holograms of a collection of small objects code the information of shape, orientation and 3D location of all objects. The recovery of the average size or orientation distribution however requires 3D reconstruction and analysis, which makes it hardly usable in on-line applications.

Key idea(s)

We show that the auto-covariance of a hologram can be inverted to recover directly the size and/or orientation information of the objects. Since inversion of the auto-covariance is difficult, only approximate sizes are available. This technique can be usefull to get a first guess on the size of particles when using the particle detection algorithm described above.

Results

The average size of particles can be recovered from a hologram of several particles spread in a volume. Short fibers have been studied. It has been shown that some information about the orientation of the projection of the fibers orientations can be recovered by inversion of the auto-covariance of the hologram.

Related publications:

"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)
 
"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)
 

Grants

DGA "SAR image regularization by minimization techniques"

2009 - 2011


Project leader

Florence Tupin, Télécom Paristech

Partners

Objectives

This project aims at comparing and applying the most recent image denoising techniques to the domain of synthetic aperture radar imaging.
Many different problems can be mapped to an energy minimization problem. The energy is generally composed of two terms: a data fidelity term (neg log-likelihood), and a regularization term that imposes a prior on the solution, often expressed as local interactions. Several recent developments in image processing are devoted to solving this minimization problem. On the one hand, discrete approaches based on minimum cut computation on graphs are very efficient techniques. They provide in several cases a deterministic way to solve exactly non-convex minimization problems. On the other hand, recent progress on variational approaches devoted to convex but non-smooth energies can be applied to handle some edge-preserving regularization models.
One of our goals is to define statistical models adapted to SAR images. We will also focus on the numerical techniques to efficiently apply these models.
 

ANR MITIV "Biomedical Image Reconstruction by Inverse Methods"

2009 - 2013


Project leader

Eric Thiébaut, Observatoire de Lyon

Partners

Objectives

This project aims at developping new models and reconstruction techniques for microscopy, angiography and dynamic tomography. Both theoretical aspects and practical issues such as automation and medical certification will be covered thanks to a pluri-disciplinary team made of signal and image reconstruction experts, software developers, microscopists, and cardiologists.
 

Co-workers

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

Publications

Papers in refereed journals

Submitted

[12] "NL-InSAR: Non-local interferogram estimation," C. Deledalle, L. Denis, and F. Tupin, oct. 2009 (pdf).
 

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)
 
[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) -- 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)
 
[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)
 
[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)
 

2008

[6] "Digital holography of particles: experimental parameters setting and benefits of the inverse problems approach," J. Gire, L. Denis, C. Fournier, C. Ducottet, E. Thiebaut, and F. Soulez, Meas. Sci. Tech., 19, 2008. (pdf, doi)
 
[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)
 

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)
 
[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)
 
[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)
 

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)
 



Conference papers

2009

[13] "Resolution in in-line digital holography," C. Fournier, L. Denis, T. Fournel, Workshop on Information Optics, Paris, France, July 2009.
 
[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)

Computer graphics (lectures + lab sessions)

Unix systems programming (lab sessions)

Signals and Linear Systems (tutorials + lab sessions)

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:

$ latex makebst
[...]
***********************************
* This is Make Bibliography Style *
***********************************
It makes up a docstrip batch job to produce
a customized .bst file for running with BibTeX
Do you want a description of the usage? (NO)

\yn=y
In the interactive dialogue that follows,
you will be presented with a series of menus.
In each case, one answer is the default, marked as (*),
and a mere carriage-return is sufficient to select it.
(If there is no * choice, then the default is the last choice.)
For the other choices, a letter is indicated
in brackets for selecting that option. If you select
a letter not in the list, default is taken.

The final output is a file containing a batch job
which may be (La)TeXed to produce the desired BibTeX
bibliography style file. The batch job may be edited
to make minor changes, rather than running this program
once again.

[...]
Name of the final OUTPUT .bst file? (default extension=bst)

\ofile=mystyle.bst

[...]
STYLE OF CITATIONS:
(*) Numerical as in standard LaTeX
(a) Author-year with some non-standard interface
(b) Alpha style, Jon90 or JWB90 for single or multiple authors
(o) Alpha style, Jon90 even for multiple authors
(f) Alpha style, Jones90 (full name of first author)
(c) Cite key (special for listing contents of bib file)
Select:

[...]
AUTHOR NAMES:
(*) Full, surname last (John Frederick Smith)
(f) Full, surname first (Smith, John Frederick)
(i) Initials + surname (J. F. Smith)
(r) Surname + initials (Smith, J. F.)
(s) Surname + dotless initials (Smith J F)
(x) Surname + pure initials (Smith JF)
(y) Surname + comma + pure initials (Smith, JF)
(z) Surname + spaceless initials (Smith J.F.)
(a) Only first name reversed, initials (AGU style: Smith, J. F., H. K. Jones)
(b) First name reversed, with full names (Smith, John Fred, Harry Kab Jones)
Select:
[...]
NUMBER OF AUTHORS:
(*) All authors included in listing
(l) Limited authors (et al replaces missing names)
Select:
[...]
TYPEFACE FOR AUTHORS IN LIST OF REFERENCES:
(*) Normal font for author names
(s) Small caps authors (\sc)
(i) Italic authors (\it or \em)
(b) Bold authors (\bf)
(u) User defined author font (\bibnamefont)
Select:
[...]

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:

\bibliographystyle{mystyle}
\bibliography{mybib}

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.

Last update: February 2010