Efficient All-in-One Image Restoration With Adaptive Frequency Enhancement

Abstract

All-in-one image restoration has recently attracted considerable attention for its ability to address multiple degradation types within a single, unified framework. However, existing methods often incur substantial computational overhead, especially when incorporating explicit degradation priors via complex auxiliary branches, hindering their practical deployment. In this paper, we propose AdaptIR, an efficient all-in-one image restoration network equipped with adaptive frequency enhancement. Recognizing that different degradations impact distinct frequency subbands and exhibit spatially varying restoration demands, we design an Adaptive Frequency Enhancement Module (AFEM) that couples frequency learning with adaptive convolutions to better capture frequency-aware information. Specifically, AFEM learns pixel-wise adaptive attention weights to modulate the spectra of dynamic convolutions, enabling spatially adaptive and content-aware restoration. Furthermore, we introduce a lightweight backbone featuring a Receptive Field Expansion Module (RFEM), which enlarges the receptive field of a convolutional U-shaped architecture by convolving wavelet-transform coefficients. By integrating the plug-and-play AFEM into the bottleneck of the baseline model, AdaptIR achieves state-of-the-art performance on all-in-one image restoration tasks involving multiple degradations, while maintaining high computational efficiency. Moreover, the proposed model can be readily extended to single-degradation tasks (e.g., dehazing, desnowing, and deraining) and domain-specific applications, including ultra-high-definition (UHD), medical, and remote sensing image restoration.