Perceptual Image Restoration with High-Quality Priori and Degradation Learning

TL;DR

This paper introduces a perceptual image restoration method that leverages a constrained generative model with MMD to produce high-quality images, effectively handling complex real-world degradations.

Contribution

It proposes a novel restriction within the prior manifold using MMD and models degradation as a conditional distribution, improving perceptual restoration quality.

Findings

Outperforms existing methods in perceptual quality

Effective on real-world complex degradations

Improves no-reference image quality assessment

Abstract

Perceptual image restoration seeks for high-fidelity images that most likely degrade to given images. For better visual quality, previous work proposed to search for solutions within the natural image manifold, by exploiting the latent space of a generative model. However, the quality of generated images are only guaranteed when latent embedding lies close to the prior distribution. In this work, we propose to restrict the feasible region within the prior manifold. This is accomplished with a non-parametric metric for two distributions: the Maximum Mean Discrepancy (MMD). Moreover, we model the degradation process directly as a conditional distribution. We show that our model performs well in measuring the similarity between restored and degraded images. Instead of optimizing the long criticized pixel-wise distance over degraded images, we rely on such model to find visual pleasing images with high probability. Our simultaneous restoration and enhancement framework generalizes well to real-world complicated degradation types. The experimental results on perceptual quality and no-reference image quality assessment (NR-IQA) demonstrate the superior performance of our method.