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Blind deblurring using coupled convolutional sparse coding regularisation for noisy-blurry image

Title
Blind deblurring using coupled convolutional sparse coding regularisation for noisy-blurry image
Authors
An, Taeg-HyunChoi, DooseopCho, SunghyunHONG, KI SANGLEE, SEUNGYONG
POSTECH Authors
HONG, KI SANGLEE, SEUNGYONG
Date Issued
Jul-2018
Publisher
IET
Abstract
This Letter proposes a novel method to deblur a blurry image corrupted by noise. The authors estimate a noise-free version of the input blurred image and a corresponding noise-free version of the latent image without damaging the blur information, as well as the latent image and blur kernel in an alternating fashion. To this end, they first propose coupled convolutional sparse coding, which incorporates the coupled dictionary concept into convolutional sparse coding. Then they model the noise-free blurred image to share the sparse coefficients with the noise-free latent image using the coupled dictionaries. By utilising these noise-free images as priors in alternating latent image estimation and blur kernel estimation steps, they can estimate a high-quality latent image and blur kernel in the presence of noise. Experimental results demonstrate that the proposed method outperforms previous methods in handling noisy blurred images.
URI
http://oasis.postech.ac.kr/handle/2014.oak/94513
DOI
10.1049/el.2018.0901
ISSN
0013-5194
Article Type
Article
Citation
Electronics Letters, vol. 54, no. 14, page. 874 - 876, 2018-07
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 HONG, KI SANG
Dept of Electrical Enginrg
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