A Perceptually Inspired New Blind Image Denoising Method Using $L_{1}$ and Perceptual Loss

Recently, discriminative learning-based denoising methods have received much attention and have been studied to a large extent because of their high denoising performance with significantly shorter inference time compared to model based denoising methods. In this paper, we consider a perceptually motivated blind image denoising problem, which removes various levels of noise from an observed noisy image with a single model and produces visually pleasant images. It is well known that very few blind image denoising methods are available and they have shown the limited quality of restored images because they sacrifice the fine image details during the denoising process due to over-smoothing, resulting in visually unpleasant images. To overcome these problems, we propose a novel loss function that encourages the network to restore noise free images by focusing on the perceived visual quality. Then, the proposed loss function is adopted to an encoder-decoder network with skip connections that produces visually pleasant images with highly preserved fine image details. Further, this method is robust and constantly performs well for several unknown noise levels. Our extensive experimental results show the superiority of the proposed method both quantitatively and qualitatively.