Joint sparse learning with nonlocal and local image priors for image error concealment

Akbari, A, Trocan, M, Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801 and Granado, B, 2019. Joint sparse learning with nonlocal and local image priors for image error concealment. IEEE Transactions on Circuits and Systems for Video Technology. ISSN 1051-8215

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Abstract

Joint sparse representation (JSR) model has recently emerged as a powerful technique with wide variety of applications. In this paper, the JSR model is extended to error concealment (EC) application, being effective to recover the original image from its corrupted version. This model is based on jointly learning a dictionary pair and two mapping matrices that are trained offline from external training images. Given the trained dictionaries and mappings, the restoration is done by transferring the recovery problem into the sparse representation domain with respect to the trained dictionaries, which is further transformed into a common space using the respective mapping matrices. Then, the reconstructed image is obtained by back projection into the spatial domain. In order to improve the accuracy and stability of the proposed JSR-based EC algorithm and avoid unexpected artifacts, the local and non-local priors are seamlessly integrated into the JSR model. The non-local prior is based on the self-similarity within natural images and helps to find an accurate sparse representation by taking a weighted average of similar areas throughout the image. The local prior is based on learning the local structural regularity of the natural images and helps to regularize the sparse representation, exploiting the strong correlation in the small local areas within the image. Compared with the state-of-the-art EC algorithms, the results show that the proposed method has better reconstruction performance in terms of objective and subjective evaluations.

Item Type:	Journal article
Publication Title:	IEEE Transactions on Circuits and Systems for Video Technology
Creators:	Akbari, A., Trocan, M., Sanei, S. and Granado, B.
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date:	10 July 2019
ISSN:	1051-8215
Identifiers:	Number Type 10.1109/tcsvt.2019.2927912 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	19 Jul 2019 08:19
Last Modified:	19 Jul 2019 08:19
URI:	https://irep.ntu.ac.uk/id/eprint/37122

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