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A Brief Review of Image Denoising Algorithms and Beyond

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Inpainting and Denoising Challenges

Part of the book series: The Springer Series on Challenges in Machine Learning ((SSCML))

Abstract

The recent advances in hardware and imaging systems made the digital cameras ubiquitous. Although the development of hardware has steadily improved the quality of images for the last several decades, image degradation is unavoidable due to the many factors affecting the image acquisition process and the subsequent post-processing. Image denoising, which aims to reconstruct a high quality image from its degraded observation, is a classical yet still very active topic in the area of low-level computer vision. It represents an important building block in real applications such as digital photography, medical image analysis, remote sensing, surveillance and digital entertainment. Also, image denoising constitutes an ideal test bed for evaluating image prior modeling methods. In this paper, we briefly review recent progresses in image denoising. We firstly present an overview of prior modeling approaches used in image denoising task. Then, we review conventional sparse representation based denoising algorithms, low-rank based denoising algorithms and recently proposed deep neural networks based approaches. At last, we discuss some emerging topics and open problems about image denoising.

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Acknowledgements

This work was partly supported by the ETH General Fund, Huawei, and Nvidia through a hardware grant.

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  1. Computer Vision Laboratory, ETH Zürich, Switzerland

    Shuhang Gu & Radu Timofte

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  1. Shuhang Gu
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Correspondence to Radu Timofte .

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Editors and Affiliations

  1. Department of Mathematics & Informatics, Universitat de Barcelona, Computer Vision Center, Barcelona, Spain

    Sergio Escalera

  2. Aix-Marseille University, Marseille, France

    Stephane Ayache

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jun Wan

  4. Computer Vision Center, Autonomous University of Barcelona, Bellaterra, Barcelona, Spain

    Meysam Madadi

  5. Radboud University Nijmegen, Nijmegen, The Netherlands

    Umut Güçlü

  6. Open University of Catalonia, Barcelona, Spain

    Xavier Baró

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Gu, S., Timofte, R. (2019). A Brief Review of Image Denoising Algorithms and Beyond. In: Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., Baró, X. (eds) Inpainting and Denoising Challenges. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-25614-2_1

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