Degradation-Aware Adaptive Context Gating for Unified Image Restoration

TL;DR

The paper introduces DACG-IR, a novel degradation-aware model for unified image restoration that dynamically modulates features based on degradation characteristics, outperforming existing methods.

Contribution

It proposes a degradation-aware adaptive gating mechanism with a multi-scale module and dual-gated fusion for improved image restoration across diverse degradations.

Findings

DACG-IR outperforms state-of-the-art methods in various restoration tasks.

The model effectively suppresses noise and preserves structures.

It handles multiple degradation types with a single model.

Abstract

Unified image restoration using a single model often faces task interference due to diverse degradations. To address this, we propose DACG-IR (Degradation-Aware Adaptive Context Gating), which enables explicit perception of degradation characteristics to dynamically modulate feature representations. Our method constructs degradation-aware contextual representations from the input to modulate attention distribution, frequency-domain features, and feature aggregation. Specifically, a lightweight multi-scale degradation-aware module extracts coarse degradation information and generates layer-wise prompts. These prompts guide attention temperature and output gating in encoder and decoder blocks for adaptive feature extraction. Additionally, a spatial-channel dual-gated adaptive fusion mechanism refines encoder features, suppressing noise propagation from shallow to deep layers. This design effectively suppresses degradation-induced noise while preserving informative structures. Experiments show DACG-IR outperforms state-of-the-art methods in single-task, all-in-one, adverse weather removal, and composite degradation settings. Code: https://github.com/HlHomes/DACG-IR-code