2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-2.4
Paper Title LABMAT: LEARNED FEATURE-DOMAIN BLOCK MATCHING FOR IMAGE RESTORATION
Authors Shijun Liang, Michigan State University, United States; Berk Iskender, University of Illinois at Urbana-Champaign, United States; Bihan Wen, Nanyang Technological University, Singapore; Saiprasad Ravishankar, Michigan State University, United States
SessionTEC-2: Restoration and Enhancement 2
LocationArea G
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
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Abstract Grouping of similar patches, called block matching, has been widely used in image restoration applications. Popular block matching algorithms exploit image non-local similarities in spatial or a fixed transform domain, e.g., wavelets and DCT. However, applying these methods on corrupted patches usually leads to degraded matching accuracy, thus limiting the image restoration performance. In this work, we develop a novel methodology for performing block matching in a supervised way by learning multi-layer sparsifying transforms. The proposed learned transform-domain block matching method for image restoration, dubbed LABMAT, is shown to have better accuracy in terms of clustering similar blocks in the presence of noise, and it also achieves an improved denoising performance when it is incorporated into popular non-local denoising schemes.