MLR : An Efficient Denoising Model for Highly Corrupted Images

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationIECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Number of pages6
ISBN (Electronic)9781665480253
ISBN (Print)978-1-6654-8026-0
Publication statusPublished - 2022

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
ISSN (Print)1553-572X
ISSN (Electronic)2577-1647

Conference

Title48th Annual Conference of the Industrial Electronics Society (IECON 2022)
LocationConvention Center SQUARE
PlaceBelgium
CityBrussels
Period17 - 20 October 2022

Abstract

Internet of Things (IoT) consists of devices that generate, process, and exchange vast amounts of images. Unfortunately, these images always contain some kinds of noise, which significantly degrades data utility after the processing center receives the images. Image denoising plays a crucial role to recover the original image approximately from its noisy image according to the features of noise distribution and the structure information of the original image. However, the existing denoising algorithms are invalid when the original images are highly corrupted due to the high computational complexity. Therefore, this paper proposes a novel denoising algorithm based on the multi-low-rank model (MLR), which successively enforces similar blocks, dictionaries, and coefficient matrices approximating to low rank, thereby gradually removing noise. Extensive experimental simulations demonstrate that the MLR algorithm has the optimal denoising performance in terms of denoising quality and efficiency, especially in the case of strong salt noise.

Research Area(s)

  • Image denoising, image discovery, multi-low-rank model, non-local model

Citation Format(s)

MLR: An Efficient Denoising Model for Highly Corrupted Images. / Yao, Shihong; Liu, Yi; Wang, Tao et al.
IECON 2022 - 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022. (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review