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The vast work in Deep Learning (DL) has led to a leap in image denoisingresearch. Most DL solutions for this task have chosen to put their efforts onthe denoiser's architecture while maximizing distortion performance. However,distortion driven solutions lead to blurry results with sub-optimal perceptualquality, especially in immoderate noise levels. In this paper we propose adifferent perspective, aiming to produce sharp and visually pleasing denoisedimages that are still faithful to their clean sources. Formally, our goal is toachieve high perceptual quality with acceptable distortion. This is attained bya stochastic denoiser that samples from the posterior distribution, trained asa generator in the framework of conditional generative adversarial networks(CGAN). Contrary to distortion-based regularization terms that conflict withperceptual quality, we introduce to the CGAN objective a theoretically foundedpenalty term that does not force a distortion requirement on individualsamples, but rather on their mean. We showcase our proposed method with a noveldenoiser architecture that achieves the reformed denoising goal and producesvivid and diverse outcomes in immoderate noise levels.

High Perceptual Quality Image Denoising with a Posterior Sampling CGAN