Efficient All-in-One Weather Restoration using Spectral Harmonization

2026-09-02

AI Summary

本文提出了一种基于频谱调和的轻量级全合一图像恢复方法，有效解决了雨、雾、雪等恶劣天气条件下的图像质量退化问题。

Abstract

Adverse weather conditions such as rain, haze, and snow significantly degrade image quality, posing challenges for both human perception and physical AI. Existing restoration methods require large computational budgets, struggling to process high-resolution images and handle different degradations. In this paper, we present Frequency Reconstruction via Spectral Harmonization, a novel lightweight all-in-one restoration method that explicitly decomposes feature representations into high- and low-frequency components at each scale of a hierarchical encoder-decoder architecture. By combining spectral decomposition with spatial processing through Fourier-based skip connections, FReSH-IR captures complementary frequency information without sacrificing spatial detail. Our approach achieves similar restoration quality with 80% fewer parameters and operations than transformer-based models. Extensive experiments demonstrate that our method offers a great efficiency-performance trade-off, highlighting its practical applications in constrained-resource systems.

Problem

• adverse weather
• image quality
• computational budget
• high-resolution images
• degradations

Innovation

• Spectral Harmonization
• Frequency Decomposition
• Hierarchical Encoder-Decoder
• Fourier-based Skip Connections
• Lightweight Restoration