DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer

TL;DR

DnSwin introduces a novel wavelet-based sliding-transformer approach for real-world image denoising, effectively capturing frequency dependencies and outperforming existing methods on benchmark datasets.

Contribution

The paper proposes a continuous wavelet sliding-transformer that leverages wavelet transforms and self-attention to improve real-world image denoising performance.

Findings

Outperforms state-of-the-art denoising methods on real-world benchmarks

Effectively captures frequency dependencies in noisy images

Enhances denoising quality by integrating wavelet transforms with transformers

Abstract

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.