Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance

TL;DR

This paper introduces DHGM, a diffusion-based model that effectively removes rain artifacts and enhances high-frequency details in images, improving clarity and detail preservation for high-resolution visual tasks.

Contribution

The paper proposes a novel diffusion-guided approach that combines diffusion priors with high-pass filters to resolve conflicts between deraining and super-resolution tasks.

Findings

DHGM outperforms existing deraining and super-resolution methods.

DHGM achieves higher image quality with lower computational costs.

The approach effectively preserves high-frequency details in degraded images.

Abstract

Clean images are crucial for visual tasks such as small object detection, especially at high resolutions. However, real-world images are often degraded by adverse weather, and weather restoration methods may sacrifice high-frequency details critical for analyzing small objects. A natural solution is to apply super-resolution (SR) after weather removal to recover both clarity and fine structures. However, simply cascading restoration and SR struggle to bridge their inherent conflict: removal aims to remove high-frequency weather-induced noise, while SR aims to hallucinate high-frequency textures from existing details, leading to inconsistent restoration contents. In this paper, we take deraining as a case study and propose DHGM, a Diffusion-based High-frequency Guided Model for generating clean and high-resolution images. DHGM integrates pre-trained diffusion priors with high-pass filters to simultaneously remove rain artifacts and enhance structural details. Extensive experiments demonstrate that DHGM achieves superior performance over existing methods, with lower costs.