Title: EvoIR: Towards All-in-One Image Restoration via Evolutionary Frequency Modulation
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Low Level, Image Restoration, All-in-One Image Restoration

Abstract: All-in-One Image Restoration (AiOIR) tasks often involve diverse degradations that require robust and versatile strategies. However, most existing approaches typically lack explicit frequency modeling and rely on fixed or heuristic optimization schedules, which limit the generalization across heterogeneous degradations. To address these limitations, we propose EvoIR, a novel framework that introduces evolutionary frequency modulation for dynamic and adaptive image restoration. Specifically, EvoIR employs the Frequency-Modulated Module (FMM) that decomposes features into high- and low-frequency branches in an explicit manner and adaptively modulates them to enhance both structural fidelity and fine-grained details. Central to EvoIR, an Evolutionary Optimization Strategy (EOS) iteratively adjusts frequency-aware objectives through a population-based evolutionary process, dynamically balancing structural accuracy and perceptual fidelity. Its evolutionary guidance further mitigates gradient conflicts across degradations and accelerates convergence. By synergizing FMM and EOS, EvoIR yields greater improvements than using either component alone, underscoring their complementary roles. Extensive experiments on multiple benchmarks demonstrate that EvoIR outperforms state-of-the-art AiOIR methods.

TL;DR: EvoIR is an All-in-One Image Restoration framework that leverages a Frequency-Modulated Module for frequency-aware feature representation, and employs an Evolutionary Optimization Strategy to adaptively balance complementary loss objectives.