116 research outputs found
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
BLADE: Filter Learning for General Purpose Computational Photography
The Rapid and Accurate Image Super Resolution (RAISR) method of Romano,
Isidoro, and Milanfar is a computationally efficient image upscaling method
using a trained set of filters. We describe a generalization of RAISR, which we
name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable
edge-adaptive filtering framework that is general, simple, computationally
efficient, and useful for a wide range of problems in computational
photography. We show applications to operations which may appear in a camera
pipeline including denoising, demosaicing, and stylization
Deep Mean-Shift Priors for Image Restoration
In this paper we introduce a natural image prior that directly represents a
Gaussian-smoothed version of the natural image distribution. We include our
prior in a formulation of image restoration as a Bayes estimator that also
allows us to solve noise-blind image restoration problems. We show that the
gradient of our prior corresponds to the mean-shift vector on the natural image
distribution. In addition, we learn the mean-shift vector field using denoising
autoencoders, and use it in a gradient descent approach to perform Bayes risk
minimization. We demonstrate competitive results for noise-blind deblurring,
super-resolution, and demosaicing.Comment: NIPS 201
Spectral Characterization of a Prototype SFA Camera for Joint Visible and NIR Acquisition
International audienceMultispectral acquisition improves machine vision since it permits capturing more information on object surface properties than color imaging. The concept of spectral filter arrays has been developed recently and allows multispectral single shot acquisition with a compact camera design. Due to filter manufacturing difficulties, there was, up to recently, no system available for a large span of spectrum, i.e., visible and Near Infra-Red acquisition. This article presents the achievement of a prototype of camera that captures seven visible and one near infra-red bands on the same sensor chip. A calibration is proposed to characterize the sensor, and images are captured. Data are provided as supplementary material for further analysis and simulations. This opens a new range of applications in security, robotics, automotive and medical fields
Semi-local Total Variation for Regularization of Inverse Problems
International audienceWe propose the discrete semi-local total variation (SLTV) as a new regularization functional for inverse problems in imaging. The SLTV favors piecewise linear images; so the main drawback of the total variation (TV), its clustering effect, is avoided. Recently proposed primal-dual methods allow to solve the corresponding optimization problems as easily and efficiently as with the classical TV
Contributions en optimisation topologique : extension de la méthode adjointe et applications au traitement d'images
De nos jours, l'optimisation topologique a été largement étudiée en optimisation de structure, problème majeur en conception de systèmes mécaniques pour l'industrie et dans les problèmes inverses avec la détection de défauts et d'inclusions. Ce travail se concentre sur les approches de dérivées topologiques et propose une généralisation plus flexible de cette méthode rendant possible l'investigation de nouvelles applications. Dans une première partie, nous étudions des problèmes classiques en traitement d'images (restauration, inpainting), et exposons une formulation commune à ces problèmes. Nous nous concentrons sur la diffusion anisotrope et considérons un nouveau problème : la super-résolution. Notre approche semble meilleure comparée aux autres méthodes. L'utilisation des dérivées topologiques souffre d'inconvénients : elle est limitée à des problèmes simples, nous ne savons pas comment remplir des trous ... Dans une seconde partie, une nouvelle méthode visant à surmonter ces difficultés est présentée. Cette approche, nommée voûte numérique, est une extension de la méthode adjointe. Ce nouvel outil nous permet de considérer de nouveaux champs d'application et de réaliser de nouvelles investigations théoriques dans le domaine des dérivées topologiques.Nowadays, topology optimization has been extensively studied in structural optimization which is a major interest in the design of mechanical systems in the industry and in inverse problems with the detection of defects or inclusions. This work focuses on the topological derivative approach and proposes a more flexible generalization of this method making it possible to address new applications. In a first part, we study classical image processing problems (restoration, inpainting), and give a common framework to theses problems. We focus on anisotropic diffusion and consider a new problem: super-resolution. Our approach seems to be powerful in comparison with other methods. Topological derivative method has some drawbacks: it is limited to simple problems, we do not know how to fill holes, ... In a second part, to overcome these difficulties, an extension of the adjoint method is presented. Named the numerical vault, it allows us to consider new fields of applications and to explore new theoretical investigations in the area of topological derivative
Advanced Restoration Techniques for Images and Disparity Maps
With increasing popularity of digital cameras, the field of Computa-
tional Photography emerges as one of the most demanding areas of
research. In this thesis we study and develop novel priors and op-
timization techniques to solve inverse problems, including disparity
estimation and image restoration.
The disparity map estimation method proposed in this thesis incor-
porates multiple frames of a stereo video sequence to ensure temporal
coherency. To enforce smoothness, we use spatio-temporal connec-
tions between the pixels of the disparity map to constrain our solution.
Apart from smoothness, we enforce a consistency constraint for the
disparity assignments by using connections between the left and right
views. These constraints are then formulated in a graphical model,
which we solve using mean-field approximation. We use a filter-based
mean-field optimization that perform efficiently by updating the dis-
parity variables in parallel. The parallel updates scheme, however, is
not guaranteed to converge to a stationary point. To compare and
demonstrate the effectiveness of our approach, we developed a new
optimization technique that uses sequential updates, which runs ef-
ficiently and guarantees convergence. Our empirical results indicate
that with proper initialization, we can employ the parallel update
scheme and efficiently optimize our disparity maps without loss of
quality. Our method ranks amongst the state of the art in common
benchmarks, and significantly reduces the temporal flickering artifacts
in the disparity maps.
In the second part of this thesis, we address several image restora-
tion problems such as image deblurring, demosaicing and super-
resolution. We propose to use denoising autoencoders to learn an
approximation of the true natural image distribution. We parametrize
our denoisers using deep neural networks and show that they learn
the gradient of the smoothed density of natural images. Based on
this analysis, we propose a restoration technique that moves the so-
lution towards the local extrema of this distribution by minimizing
the difference between the input and output of our denoiser. Weii
demonstrate the effectiveness of our approach using a single trained
neural network in several restoration tasks such as deblurring and
super-resolution. In a more general framework, we define a new
Bayes formulation for the restoration problem, which leads to a more
efficient and robust estimator. The proposed framework achieves state
of the art performance in various restoration tasks such as deblurring
and demosaicing, and also for more challenging tasks such as noise-
and kernel-blind image deblurring.
Keywords. disparity map estimation, stereo matching, mean-field
optimization, graphical models, image processing, linear inverse prob-
lems, image restoration, image deblurring, image denoising, single
image super-resolution, image demosaicing, deep neural networks,
denoising autoencoder
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