  Real-world image denoising is a practical image restoration problem that aims
to obtain clean images from in-the-wild noisy inputs. Recently, the Vision
Transformer (ViT) has exhibited a strong ability to capture long-range
dependencies, and many researchers have attempted to apply the ViT to image
denoising tasks. However, a real-world image is an isolated frame that makes
the ViT build long-range dependencies based on the internal patches, which
divides images into patches, disarranges noise patterns and damages gradient
continuity. In this article, we propose to resolve this issue by using a
continuous Wavelet Sliding-Transformer that builds frequency correspondences
under real-world scenes, called DnSwin. Specifically, we first extract the
bottom features from noisy input images by using a convolutional neural network
(CNN) encoder. The key to DnSwin is to extract high-frequency and low-frequency
information from the observed features and build frequency dependencies. To
this end, we propose a Wavelet Sliding-Window Transformer (WSWT) that utilizes
the discrete wavelet transform (DWT), self-attention and the inverse DWT (IDWT)
to extract deep features. Finally, we reconstruct the deep features into
denoised images using a CNN decoder. Both quantitative and qualitative
evaluations conducted on real-world denoising benchmarks demonstrate that the
proposed DnSwin performs favorably against the state-of-the-art methods.
