Research on Rain Removal Method for High Scale Rain Pattern Image Block Based on Sparse Representation Model

Abstract

This paper addresses the problem of rain removal in high-rain-texture images by proposing a novel processing method that integrates a sparse representation model. This approach aims to overcome the processing bottlenecks exhibited by traditional image rain removal techniques and deep learning methods in scenarios with dense rain textures. The paper introduces a sparse representation framework with the ability to model rain texture prior features, enabling the model to preserve natural background details in the image while avoiding excessive smoothing caused by the lack of targeted modeling during the processing. By constructing a sparse dictionary specifically tailored to rain texture structures and leveraging their statistical characteristics in both the frequency and spatial domains, the system achieves significantly superior performance in high-rain-texture regions compared to conventional non-structured methods, supported by structured modeling. To enhance the model's adaptability to complex scenes, this paper integrates a Generative Adversarial Network (GAN) with the sparse representation mechanism, enabling the entire rain removal system to simultaneously possess both representational and generative capabilities during image reconstruction. The results show that the proposed algorithm achieves significant performance improvements on the publicly available synthetic datasets Rain100H and Rain200L, outperforming most existing mainstream methods in both PSNR and SSIM. Especially in terms of visualization, the generated images achieve an ideal balance between preserving texture details and reducing rain texture interference, fully demonstrating the practical value and theoretical feasibility of this method in high-density rain texture environments.