All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network

TL;DR

This paper introduces CWP-Net, a causal-deconfounding wavelet-disentangled prompt network, to improve all-in-one image restoration by explicitly disentangling features and addressing biases, resulting in superior performance.

Contribution

The paper proposes a novel causal analysis framework and a wavelet-based disentanglement network for more effective and generalizable all-in-one image restoration.

Findings

CWP-Net outperforms state-of-the-art AiOIR methods in experiments.

Disentangling degradation and semantic features improves restoration quality.

Wavelet prompt blocks effectively generate alternative variables for causal deconfounding.

Abstract

Image restoration represents a promising approach for addressing the inherent defects of image content distortion. Standard image restoration approaches suffer from high storage cost and the requirement towards the known degradation pattern, including type and degree, which can barely be satisfied in dynamic practical scenarios. In contrast, all-in-one image restoration (AiOIR) eliminates multiple degradations within a unified model to circumvent the aforementioned issues. However, according to our causal analysis, we disclose that two significant defects still exacerbate the effectiveness and generalization of AiOIR models: 1) the spurious correlation between non-degradation semantic features and degradation patterns; 2) the biased estimation of degradation patterns. To obtain the true causation between degraded images and restored images, we propose Causal-deconfounding Wavelet-disentangled Prompt Network (CWP-Net) to perform effective AiOIR. CWP-Net introduces two modules for decoupling, i.e., wavelet attention module of encoder and wavelet attention module of decoder. These modules explicitly disentangle the degradation and semantic features to tackle the issue of spurious correlation. To address the issue stemming from the biased estimation of degradation patterns, CWP-Net leverages a wavelet prompt block to generate the alternative variable for causal deconfounding. Extensive experiments on two all-in-one settings prove the effectiveness and superior performance of our proposed CWP-Net over the state-of-the-art AiOIR methods.