Image restoration and enhancement: Recent advances and applications

Shao, Ling, Gao, Xinbo and Li, Houqiang (2014) Image restoration and enhancement: Recent advances and applications. Signal Processing, 103. pp. 1-5. ISSN 0165-1684

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Official URL: http://dx.doi.org/10.1016/j.sigpro.2014.04.028

Abstract

Image restoration and enhancement is a classical research area in image processing. Previously, adaptive local and nonlocal approximations have been popular. Local approximations attempt to estimate the image content in a locally adaptive neighbourhood. Nonlocal methods exploit the self-similarity within the whole image without the constraint of locality. The former tends to be more efficient and the latter would produce better results. Recently, learning-based techniques adopting advances in machine learning and computer vision, such as sparse coding and dictionary learning, have attracted much more attention and been applied to image/video restoration and enhancement. These techniques can represent image contents better using learned dictionaries. In addition, some novel application areas, e.g., legacy photos and paintings, HD/3D displays, mobile and portable devices, and web-scale data, have prompted new research interests in image/video restoration and enhancement.

Item Type: Article
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Related URLs:
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 12:03
Last Modified: 03 Nov 2016 12:41
URI: http://nrl.northumbria.ac.uk/id/eprint/22818

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