Grouped Multi-Scale Network for Real-World Image Denoising

Deep learning-based methods have surpassed the traditional methods in image denoising due to the prior knowledge accumulated on the large dataset. Because of the difference between additive Gaussian white noise (AWGN) and real noise, researchers have recently paid more attention to real-world image denoising. Based on the characteristics of real-world image denoising, we revise the method of noise synthesis and design a novel network to make full use of the multi-scale context. Our proposed approach dramatically surpasses all the contemporary approaches on the sRGB track of the DND benchmark [1] with PSNR over 40 dB and SSIM over 0.96.