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Joint denoising and decompression using CNN regularization
AbstractWavelet 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
[pdf][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 = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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