We studied the modern deep convolutional neural networks used for image denoising, where RGB input images are transformed into RGB output images via feed-forward convolutional neural networks that use a loss defined in the RGB color space. Considering the difference between human visual perception and objective evaluation metrics such as PSNR or SSIM, we propose a data augmentation technique and demonstrate that it is equivalent to defining a perceptual loss function. We trained a network based on this and obtained visually pleasing denoised results. We also combine an unsupervised design and the bias-free network to deal with the overfitting due to the absence of clean images, and improve performance when the noise level exceeds the training range.
Yi Yang, Chih-Hsien Chou, Jan P. Allebach, "Mix-loss trained bias-removed blind image denoising network" in Electronic Imaging, 2022, pp 288-1 - 288-7, https://doi.org/10.2352/EI.2022.34.8.IMAGE-288