Blind image deblurring with modified richardson-lucy deconvolution for ringing artifact suppression

Authors:
Hao-Liang Yang;Yen-Hao Chiao;Po-Hao Huang;Shang-Hong Lai
Affiliations:
Dept. of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Dept. of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Dept. of Computer Science, National Tsing Hua University, Hsinchu, Taiwan;Dept. of Computer Science, National Tsing Hua University, Hsinchu, Taiwan
Venue:
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Year:
2011

Citing 9
Cited 0

Gradient domain high dynamic range compression

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Bilateral Filtering for Gray and Color Images

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Removing camera shake from a single photograph

ACM SIGGRAPH 2006 Papers
Image deblurring with blurred/noisy image pairs

ACM SIGGRAPH 2007 papers
Image and depth from a conventional camera with a coded aperture

ACM SIGGRAPH 2007 papers
Motion-invariant photography

ACM SIGGRAPH 2008 papers
High-quality motion deblurring from a single image

ACM SIGGRAPH 2008 papers
Progressive inter-scale and intra-scale non-blind image deconvolution

ACM SIGGRAPH 2008 papers
Two-phase kernel estimation for robust motion deblurring

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we develop a unified image deblurring framework that consists of both blur kernel estimation and non-blind image deconvolution. For blind kernel estimation, we propose a patch selection procedure and integrate it with a coarse-to-fine kernel estimation algorithm to develop a robust blur kernel estimation algorithm. For the non-blind image deconvolution, we modify the traditional Richardson-Lucy (RL) image restoration algorithm to suppress the notorious ringing artifact in the regions around strong edges. Experimental results on some real blurred images are shown to demonstrate the improved efficiency and image restoration by using the proposed algorithm.