A multi-scale generative adversarial network for real-world image denoising

With the rising popularity and wide applications of image processing technologies in recent years, various deep learning methods have been proposed for image denoising. However, since most of such methods focus mainly on synthetic noises, their denoising effects on the spatially variant real-world noises could be further improved with more sophisticated network and training schemes. In this paper, a multi-scale generative adversarial network (MSGAN) that employs a novel network architecture and a well-designed training scheme is proposed. Specifically, a cascade multi-scale module is proposed as a basic building block of MSGAN to make use of the multi-scale context and increase the network learning capacity first, and then, a spatial attention mechanism is applied onto MSGAN to refine the denoising results. Finally, a sophisticated training scheme, which combines the pixel-level loss with the adversarial loss, is designed to suppress the real-world noises while restore both high-frequency and low-frequency image details simultaneously. Extensive experiments are conducted with several typical datasets to verify the effectiveness of MSGAN. Results demonstrate that MSGAN is promising for real-world image denoising in terms of both quantitative metrics (PSNR, SSIM) and visual quality.