A single image deraining algorithm guided by text generation based on depth information conditions

Abstract

Currently, image denoising algorithms based on text-to-image diffusion models often encounter issues with disordered internal structure layouts and discrepancies in detail when generating high-resolution images. To address these issues, we proposed a single image deraining algorithm guided by text generation based on depth information conditions. We designed a depth information encoder aimed at leveraging the depth information in rainy images to enhance the spatial mapping between text-to-image and image-to-text, thereby improving the internal structural layout of the generated images. To make the texture details of the generated image domain more similar to those of the original image domain, we designed a Cross Attention module that uses difference information to make the images in both domains more similar, thereby enhancing the guidance of existing deraining algorithms. Experimental results on multiple benchmark datasets demonstrate that the proposed algorithm outperforms state-of-the-art image deraining methods in both visual quality and quantitative performance. On average, it achieves an improvement of 0.46 in SSIM and 0.79 dB in PSNR, effectively removing rain streaks while preserving fine image details and maintaining structural consistency. We will release our code on Github.