1,091 research outputs found
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Handling noise in image deconvolution with local/non-local priors
International audienceNon-blind deconvolution consists in recovering a sharp latent image from a blurred image with a known kernel. Decon-volved images usually contain unpleasant artifacts due to the ill-posedness of the problem even when the kernel is known. Making use of natural sparse priors has shown to reduce ring-ing artifacts but handling noise remains limited. On the other hand, non-local priors have shown to give the best results in image denoising. We propose in this paper to combine both local and non-local priors to handle noise. We show that the blur increases the self-similarity within an image and thus makes non-local priors a good choice for denoising blurred images. However, denoising introduces outliers which are not Gaussian and should be well modeled. Experiments show that our method produces a better image reconstruction both visually and empirically compared to methods some popular methods
Understanding Kernel Size in Blind Deconvolution
Most blind deconvolution methods usually pre-define a large kernel size to
guarantee the support domain. Blur kernel estimation error is likely to be
introduced, yielding severe artifacts in deblurring results. In this paper, we
first theoretically and experimentally analyze the mechanism to estimation
error in oversized kernel, and show that it holds even on blurry images without
noises. Then to suppress this adverse effect, we propose a low rank-based
regularization on blur kernel to exploit the structural information in degraded
kernels, by which larger-kernel effect can be effectively suppressed. And we
propose an efficient optimization algorithm to solve it. Experimental results
on benchmark datasets show that the proposed method is comparable with the
state-of-the-arts by accordingly setting proper kernel size, and performs much
better in handling larger-size kernels quantitatively and qualitatively. The
deblurring results on real-world blurry images further validate the
effectiveness of the proposed method.Comment: Accepted by WACV 201
Dictionary Learning of Convolved Signals
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation of them. Starting from this motivation, we develop a block coordinate descent method which aims to learn a convolved dictionary and provide a sparse representation of the observed signals with small residual norm. We compare the proposed approach to the K-SVD dictionary learning algorithm and show through numerical experiment on synthetic signals that, provided some conditions on the problem data, our technique converges in a fixed number of iterations to a sparse representation with smaller residual norm
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