1,107 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
"Plug-and-Play" Edge-Preserving Regularization
In many inverse problems it is essential to use regularization methods that
preserve edges in the reconstructions, and many reconstruction models have been
developed for this task, such as the Total Variation (TV) approach. The
associated algorithms are complex and require a good knowledge of large-scale
optimization algorithms, and they involve certain tolerances that the user must
choose. We present a simpler approach that relies only on standard
computational building blocks in matrix computations, such as orthogonal
transformations, preconditioned iterative solvers, Kronecker products, and the
discrete cosine transform -- hence the term "plug-and-play." We do not attempt
to improve on TV reconstructions, but rather provide an easy-to-use approach to
computing reconstructions with similar properties.Comment: 14 pages, 7 figures, 3 table
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