Paper Info
Title
Shift-Variant Blind Deconvolution Using A Field Of Kernels
Abstract
Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variational-Bayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches.
Year
DOI
Venue
2017
10.1587/transinf.2016PCP0013
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
blind deconvolution, deblurring, shift-variant, variational Bayes
Computer vision,Pattern recognition,Deblurring,Blind deconvolution,Computer science,Wiener deconvolution,Artificial intelligence
Journal
Volume
Issue
ISSN
E100D
9
1745-1361
Citations 
PageRank 
References 
0
0.34
23
Authors
3
Name
Order
Citations
PageRank
Motoharu Sonogashira111.37
Masaaki Iiyama21714.23
Michihiko Minoh334958.69