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Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network

Cited 0 time in Web of Science Cited 2 time in Scopus
Authors

Jang, Yeong Il; Lee, Keuntek; Park, Gu Yong; Kim, Seyun; Cho, Nam Ik

Issue Date
2023
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), pp.12162-12171
Abstract
There have been many image denoisers using deep neural networks, which outperform conventional model-based methods by large margins. Recently, self-supervised methods have attracted attention because constructing a large real noise dataset for supervised training is an enormous burden. The most representative self-supervised denoisers are based on blind-spot networks, which exclude the receptive field's center pixel. However, excluding any input pixel is abandoning some information, especially when the input pixel at the corresponding output position is excluded. In addition, a standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise, though it successfully removes independently distributed synthetic noise. Hence, to realize a more practical denoiser, we propose a novel self-supervised training framework that can remove real noise. For this, we derive the theoretic upper bound of a supervised loss where the network is guided by the downsampled blinded output. Also, we design a conditional blind-spot network (C-BSN), which selectively controls the blindness of the network to use the center pixel information. Furthermore, we exploit a random subsampler to decorrelate noise spatially, making the C-BSN free of visual artifacts that were often seen in downsample-based methods. Extensive experiments show that the proposed C-BSN achieves state-of-the-art performance on real-world datasets as a self-supervised denoiser and shows qualitatively pleasing results without any post-processing or refinement.
ISSN
1550-5499
URI
https://hdl.handle.net/10371/201056
DOI
https://doi.org/10.1109/ICCV51070.2023.01120
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