Authors:
Khouloud Guemri
1
;
Fadoua Drira
1
;
Rim Walha
1
;
Adel M. Alimi
1
and
Frank LeBourgeois
2
Affiliations:
1
ReGIM-lab, Tunisia
;
2
University of Lyon, France
Keyword(s):
Blind Image Deblurring, Sparse Representation, Edge based Information, Kernel Estimation, Deconvolution.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
Abstract:
Blind image deblurring is the estimation of the blur kernel and the latent sharp image from a blurry image.
This makes it a significant ill-posed problem with various investigations looking for adequate solutions. The
recourse to image priors have been noticed in recent approaches to improve final results. One of the most
interesting results are based on data priors. This has been the starting point to the proposed blind image
deblurring system. In particular, this study explores the potential of the sparse representation widely known
for its efficiency in several reconstruction tasks. In fact, we propose a sparse representation based iterative
deblurring method that exploits sparse constraints of edge based image patches. This process includes the K-SVD
algorithm useful for the dictionary definition. Our main contributions are (1) the application of a shock
filter as a pre-processing step followed by filter sub-bands applications for an effective contour detection, (2)
the
use of an online training data-sets with elementary patterns to describe edge-based information and (3)
the recourse to an adaptative dictionary training. The experimental study illustrates promising results of the
proposed deblurring method compared to the well-known state-of-the-art methods.
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