On combining denoising with learning-based image decoding

Léo Larigauderie; Michela Testolina; Touradj Ebrahimi

doi:10.1117/12.2636682

3 October 2022 On combining denoising with learning-based image decoding

Léo Larigauderie, Michela Testolina, Touradj Ebrahimi

Proceedings Volume 12226, Applications of Digital Image Processing XLV; 122260M (2022) https://doi.org/10.1117/12.2636682
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

Noise is an intrinsic part of any sensor and is present, in various degrees, in any content that has been captured in real life environments. In imaging applications, several pre- and post-processing solutions have been proposed to cope with noise in captured images. More recently, learning-based solutions have shown impressive results in image enhancement in general, and in image denoising in particular. In this paper, we review multiple novel solutions for image denoising in the compressed domain, by integrating denoising operations into the decoder of a learning-based compression method. The paper starts by explaining the advantages of such an approach from different points of view. We then describe the proposed solutions, including both blind and non-blind methods, comparing them to state of the art methods. Finally, conclusions are drawn from the obtained results, summarizing the advantages and drawbacks of each method.

Conference Presentation

Citation Download Citation

Léo Larigauderie, Michela Testolina, and Touradj Ebrahimi "On combining denoising with learning-based image decoding", Proc. SPIE 12226, Applications of Digital Image Processing XLV, 122260M (3 October 2022); https://doi.org/10.1117/12.2636682

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