Image and video restoration and enhancement via sparse representation

Li Wei Kang*, Chih Yang Lin, Chia Mu Yu, Chia Hung Yeh

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

The chapter provides a survey of recent advances in image/video restoration and enhancement via spare representation. Images/videos usually unavoidably suffer from noises due to sensor imperfection or poor illumination. Numerous contributions have addressed this problem from diverse points of view. Recently, the use of sparse and redundant representations over learned dictionaries has become one specific approach. One goal here is to provide a survey of advances in image/video denoising via sparse representation. Moreover, to consider more general types of noise, this chapter also addresses the problems about removals of structured/unstructured components (e.g., rain streaks or blocking artifacts) from image/video. Moreover, image/video quality may be degraded from low-resolution due to low-cost acquisition. Hence, this chapter also provides a survey of recently advances in super-resolution via sparse representation. Finally, the conclusion can be drawn that sparse representation techniques have been reliable solutions in several problems of image/video restoration and enhancement.

Original languageEnglish
Title of host publicationBiometrics
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages501-528
Number of pages28
ISBN (Electronic)9781522509844
ISBN (Print)9781522509837
DOIs
StatePublished - 30 Aug 2016

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    Kang, L. W., Lin, C. Y., Yu, C. M., & Yeh, C. H. (2016). Image and video restoration and enhancement via sparse representation. In Biometrics: Concepts, Methodologies, Tools, and Applications (pp. 501-528). IGI Global. https://doi.org/10.4018/978-1-5225-0983-7.ch021