From Physical Degradation Models to Task-Aware All-in-One Image Restoration

TL;DR

This paper introduces OPIR, a task-aware, physically-based image restoration framework that efficiently handles multiple degradation types with a unified model, emphasizing reliability and real-time applicability.

Contribution

The paper proposes a novel two-stage, task-aware inverse degradation operator prediction method that improves efficiency and reliability in all-in-one image restoration.

Findings

Outperforms existing methods in all-in-one restoration tasks.

Achieves real-time performance through efficient convolution acceleration.

Demonstrates superior results on diverse degradation scenarios.

Abstract

All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing prompt information or leveraging large models, the added learning modules increase system complexity and hinder real-time applicability. In this paper, we adopt a physical degradation modeling perspective and predict a task-aware inverse degradation operator for efficient all-in-one image restoration. The framework consists of two stages. In the first stage, the predicted inverse operator produces an initial restored image together with an uncertainty perception map that highlights regions difficult to reconstruct, ensuring restoration reliability. In the second stage, the restoration is further refined under the guidance of this uncertainty map. The same inverse operator prediction network is used in both stages, with task-aware parameters introduced after operator prediction to adapt to different degradation tasks. Moreover, by accelerating the convolution of the inverse operator, the proposed method achieves efficient all-in-one image restoration. The resulting tightly integrated architecture, termed OPIR, is extensively validated through experiments, demonstrating superior all-in-one restoration performance while remaining highly competitive on task-aligned restoration.