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Journal Article Blind deblurring using coupled convolutional sparse coding regularisation for noisy‐blurry images
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Authors
T.-H. An, D. Choi, S. Cho, K.-S. Hong, S. Lee
Issue Date
2018-07
Citation
Electronics Letters, v.54, no.14, pp.874-875
ISSN
0013-5194
Publisher
IET
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1049/el.2018.0901
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.
KSP Keywords
Blind deblurring, Blur Kernel Estimation, Blurred image, Convolutional sparse coding, High-quality, Image Estimation, coupled dictionaries, latent image, novel method, sparse coding(SC), sparse coefficients