Title
Understanding Blind Deconvolution Algorithms
Abstract
Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. We show that, using reasonable image priors, a naive simulations MAP estimation of both latent image and blur kernel is guaranteed to fail even with infinitely large images sampled from the prior. On the other hand, we show that since the kernel size is often smaller than the image size, a MAP estimation of the kernel alone is well constrained and is guaranteed to succeed to recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. As a first step toward this experimental evaluation, we have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrate that the shift-invariant blur assumption made by most algorithms is often violated.
Year
DOI
Venue
2011
10.1109/TPAMI.2011.148
Pattern Analysis and Machine Intelligence, IEEE Transactions
Keywords
Field
DocType
deconvolution,image restoration,maximum likelihood estimation,MAP estimation,blind deconvolution algorithms,blur kernel,blurred image,sharp version,Blind deconvolution,motion deblurring,natrual image statistics,statistical estimation.
Blind deconvolution,Computer science,Deconvolution,Artificial intelligence,Image restoration,Kernel (linear algebra),Computer vision,Pattern recognition,Convolution,Algorithm,Ground truth,Prior probability,Image resolution
Journal
Volume
Issue
ISSN
33
12
0162-8828
Citations 
PageRank 
References 
107
2.75
20
Authors
4
Search Limit
100107
Name
Order
Citations
PageRank
Anat Levin13578212.90
Yair Weiss210240834.60
Frédo Durand38625414.94
William T. Freeman4173821968.76