Joint denoising and decompression using CNN regularization

Mario Gonzalez, Javier Preciozzi, Pablo Muse, Andres Almansa; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2598-2601

Abstract


Wavelet compression schemes such as JPEG2000 may lead to very specific visual artifacts due to quantization of noisy wavelet coefficients. These artifacts have highly spatially-correlated structure, making it difficult to be re- moved with standard denoising algorithms. In this work, we propose a joint denoising and decompression method that combines a data-fitting term, which takes into account the quantization process, and an implicit prior learnt using a state-of-the-art denoising CNN.

Related Material


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[bibtex]
@InProceedings{Gonzalez_2018_CVPR_Workshops,
author = {Gonzalez, Mario and Preciozzi, Javier and Muse, Pablo and Almansa, Andres},
title = {Joint denoising and decompression using CNN regularization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}