A Semi-Supervised Single-Image Deraining Algorithm Based on the Integration of Wavelet Transform and Swin Transformer

Abstract

Rain is a typical meteorological event that affects the visual appeal of outdoor pictures. The presence of rain streaks severely blurs image details, negatively impacting subsequent computer visual tasks. Due to the challenge of acquiring authentic photographs of rainfall, most deraining methods have been developed using generated samples. However, the inherent differences between generated and real data lead to poor generalization performance in practical applications. This study proposes a semi-supervised single-image rain removal approach using Transformer and wavelet transform. It fully utilizes the feature information of rainy images, addressing the issue that current methods focus too much on network structure innovation while neglecting rain streak features. The algorithm leverages the directional properties of wavelet transform to decompose rainy images into multi-scale components, with networks of varying sizes generating rain streak maps across different directions and scales. By combining supervised and unsupervised training in a semi-supervised system, the model improves deraining performance and generalization capability. Additionally, a residual detail recovery network restores fine-grained image details, further enhancing the deraining effect in real-world scenarios. Comprehensive tests on multiple standard datasets show that the proposed approach outperforms current methods, confirming its effectiveness in practical applications. Experimental results on common datasets demonstrate that it performs better than advanced rain removal algorithms. The method’s superiority is further validated by the PSNR and SSIM values of 34.86 dB and 0.961 on the Rain1200 synthetic dataset, and the NIQE and PIQE values of 11.52 and 9.13 on the RealRain dataset.