Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration

TL;DR

This paper introduces DA-RCOT, a novel optimal transport-based method for unified image restoration that adaptively handles multiple degradation types by modeling residuals as degradation cues, achieving superior performance and robustness.

Contribution

The paper proposes a degradation-aware residual-conditioned optimal transport framework that models all-in-one image restoration as an OT problem with residual-guided conditioning, enabling dynamic adaptation to various degradations.

Findings

Outperforms state-of-the-art methods across five degradation types.

Demonstrates robustness to multiple degradations and varying degradation levels.

Achieves better distortion, perceptual quality, and structure preservation.

Abstract

All-in-one image restoration has emerged as a practical and promising low-level vision task for real-world applications. In this context, the key issue lies in how to deal with different types of degraded images simultaneously. In this work, we present a Degradation-Aware Residual-Conditioned Optimal Transport (DA-RCOT) approach that models (all-in-one) image restoration as an optimal transport (OT) problem for unpaired and paired settings, introducing the transport residual as a degradation-specific cue for both the transport cost and the transport map. Specifically, we formalize image restoration with a residual-guided OT objective by exploiting the degradation-specific patterns of the Fourier residual in the transport cost. More crucially, we design the transport map for restoration as a two-pass DA-RCOT map, in which the transport residual is computed in the first pass and then encoded as multi-scale residual embeddings to condition the second-pass restoration. This conditioning process injects intrinsic degradation knowledge (e.g., degradation type and level) and structural information from the multi-scale residual embeddings into the OT map, which thereby can dynamically adjust its behaviors for all-in-one restoration. Extensive experiments across five degradations demonstrate the favorable performance of DA-RCOT as compared to state-of-the-art methods, in terms of distortion measures, perceptual quality, and image structure preservation. Notably, DA-RCOT delivers superior adaptability to real-world scenarios even with multiple degradations and shows distinctive robustness to both degradation levels and the number of degradations.