Image restoration and enhancement: Recent advances and applications

Ling Shao, Xinbo Gao, Houqiang Li

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)1-5
JournalSignal Processing
Publication statusPublished - Oct 2014


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