Blind Image Deconvolution

By Subhasis Chaudhuri, R. Velmurugan, Renu M. Rameshan. Cambridge International Law Journal, 2014

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Modern regularization methods for inverse problems (2018, methodology): “nvolution Following up on Example 6.1, an obvious nonconvex extension of the problem of deconvolution is blind deconvolution, where the convolution kernel that degrades the image is also unknown (cf. [252, 118, 93]). We basically follow the setup of [27, Section 6.2], where we assume F(K(u;h);f) = 1”
Pixel Recursive Super Resolution (2017, background): “, deconvolution [23]) to synthesizing plausible novel details de novo [33, 44].”
Blind Deconvolution Using Convex Programming (2012, methodology): “A general overview for blind deconvolution techniques in imaging (including methods based on parametric modeling of the inputs and incorporating spatial constraints) can be found in [10].”
An Overview of Low-Rank Matrix Recovery From Incomplete Observations (2016, methodology): “The lifting technique described above for blind deconvolution is just one of many methods that have been proposed for this important problem (see the books [22,67] or the survey [132] for an overview of algorithms used in digital communications, imaging, and other areas).”
On Bayesian Adaptive Video Super Resolution (2014, background): “Therefore, a practical super resolution system should simultaneously estimate optical flow [12], noise level [23], and blur kernel [16] in addition to reconstructing the highresolution image.”