Open AccessBlind deblurring using coupled convolutional sparse coding regularisation for noisy‐blurry images
Author(s) -
An T.H.,
Choi D.,
Cho S.,
Hong K.S.,
Lee S.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.0901
Subject(s) - deblurring , latent image , artificial intelligence , kernel (algebra) , image restoration , computer science , computer vision , kernel density estimation , pattern recognition (psychology) , noise (video) , neural coding , image (mathematics) , mathematics , image processing , statistics , combinatorics , estimator
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.