746 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
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Deep learning models have gained great success in many real-world
applications. However, most existing networks are typically designed in
heuristic manners, thus lack of rigorous mathematical principles and
derivations. Several recent studies build deep structures by unrolling a
particular optimization model that involves task information. Unfortunately,
due to the dynamic nature of network parameters, their resultant deep
propagation networks do \emph{not} possess the nice convergence property as the
original optimization scheme does. This paper provides a novel proximal
unrolling framework to establish deep models by integrating experimentally
verified network architectures and rich cues of the tasks. More importantly, we
\emph{prove in theory} that 1) the propagation generated by our unrolled deep
model globally converges to a critical-point of a given variational energy, and
2) the proposed framework is still able to learn priors from training data to
generate a convergent propagation even when task information is only partially
available. Indeed, these theoretical results are the best we can ask for,
unless stronger assumptions are enforced. Extensive experiments on various
real-world applications verify the theoretical convergence and demonstrate the
effectiveness of designed deep models
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