  While recent years have witnessed a dramatic upsurge of exploiting deep
neural networks toward solving image denoising, existing methods mostly rely on
simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG
compression noise and camera sensor noise, and a general-purpose blind
denoising method for real images remains unsolved. In this paper, we attempt to
solve this problem from the perspective of network architecture design and
training data synthesis. Specifically, for the network architecture design, we
propose a swin-conv block to incorporate the local modeling ability of residual
convolutional layer and non-local modeling ability of swin transformer block,
and then plug it as the main building block into the widely-used image-to-image
translation UNet architecture. For the training data synthesis, we design a
practical noise degradation model which takes into consideration different
kinds of noise (including Gaussian, Poisson, speckle, JPEG compression, and
processed camera sensor noises) and resizing, and also involves a random
shuffle strategy and a double degradation strategy. Extensive experiments on
AGWN removal and real image denoising demonstrate that the new network
architecture design achieves state-of-the-art performance and the new
degradation model can help to significantly improve the practicability. We
believe our work can provide useful insights into current denoising research.
