Indexed by:
Abstract:
Image deblurring is an ill-posed linear inverse problem. Most traditional algorithms suffer from severe ringing artifacts. Recent approaches handle this issue by regularization techniques based on assumed image prior models. This paper presents a new method to reduce the ringing artifacts, without introducing any image prior models. For this purpose, we revisit the deblurring problem, using a probabilistic graph to model the image formation process. We establish the link between iterative back-projection and belief propagation and show that the ringing artifacts are caused by error propagation. Based on these analysis, we introduce a method to measure the variance of an estimation image and further propose an error-variance aware deblurring algorithm. Experimental results demonstrate that the proposed algorithm is very effective in suppressing the ringing artifacts. © 2011 IEEE.
Keyword:
Reprint Author's Address:
Email:
Source :
ISSN: 1522-4880
Year: 2011
Page: 701-704
Language: English
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 6
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 2