“…In parallel to the above and seemingly detached from the deep learning activity, image denoising has been also a topic of investigation and discoveries of a different flavor: Harnessing denoiser engines for other imaging tasks. This started with the surprising idea that a good performing denoiser can serve as a prior, offering a highly effective regularization to inverse problems [295,231,28,139,283,268,192,280,49,55]. This continued with the discovery that such denoisers can also be used for randomly synthesizing images by offering a practical sampling from the prior distribution of images, this way posing a potent competition to Generative Adversarial Networks (GANs) and other image generation methods [260,261,262,120,287,68,122,143,121].…”