Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation

TL;DR

This paper introduces IMDNet, a novel neural network that effectively restores images degraded by multiple simultaneous factors by decoupling degradation ingredients and adaptively selecting restoration paths, outperforming existing methods.

Contribution

The paper proposes a new multi-degradation image restoration network with ingredient decoupling and task-aware path adaptation, enabling better handling of complex real-world degradations.

Findings

Superior performance on multi-degradation restoration tasks

Effective separation of degradation ingredients

Flexible path selection for diverse degradation conditions

Abstract

Image restoration (IR) aims to recover clean images from degraded observations. Despite remarkable progress, most existing methods focus on a single degradation type, whereas real-world images often suffer from multiple coexisting degradations, such as rain, noise, and haze coexisting in a single image, which limits their practical effectiveness. In this paper, we propose an adaptive multi-degradation image restoration network that reconstructs images by leveraging decoupled representations of degradation ingredients to guide path selection. Specifically, we design a degradation ingredient decoupling block (DIDBlock) in the encoder to separate degradation ingredients statistically by integrating spatial and frequency domain information, enhancing the recognition of multiple degradation types and making their feature representations independent. In addition, we present fusion block (FBlock) to integrate degradation information across all levels using learnable matrices. In the decoder, we further introduce a task adaptation block (TABlock) that dynamically activates or fuses functional branches based on the multi-degradation representation, flexibly selecting optimal restoration paths under diverse degradation conditions. The resulting tightly integrated architecture, termed IMDNet, is extensively validated through experiments, showing superior performance on multi-degradation restoration while maintaining strong competitiveness on single-degradation tasks.