This paper comprehensively reviews the recent development of image deblurring, including
non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques …

Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature
of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but …

We describe a learning-based approach to blind image deconvolution. It uses a deep
layered architecture, parts of which are borrowed from recent work on neural network …

Blind image deconvolution is an ill-posed problem that requires regularization to solve.
However, many common forms of image prior used in this setting have a major drawback in …

Most modern commodity imaging systems we use directly for photography—or indirectly rely
on for downstream applications—employ optical systems of multiple lenses that must …

Image restoration algorithms for atmospheric turbulence are known to be much more
challenging to design than traditional ones such as blur or noise because the distortion …

Photographs taken in low-light conditions are often blurry as a result of camera shake, ie a
motion of the camera while its shutter is open. Most existing deblurring methods model the …

Fully updated throughout, with several new chapters, this second edition of Introduction to
Inverse Problems in Imaging guides advanced undergraduate and graduate students in …

Fast and accurate simulation of imaging through atmospheric turbulence is essential for
developing turbulence mitigation algorithms. Recognizing the limitations of previous …

Although many long-range imaging systems are designed to support extended vision
applications, a natural obstacle to their operation is degradation due to atmospheric …