Perceptual Image Restoration with High-Quality Priori and Degradation Learning
This work addresses the problem of generating high-fidelity restored images for applications like photography and computer vision, though it appears incremental by building on prior generative model approaches.
The paper tackles perceptual image restoration by restricting solutions to the prior manifold using Maximum Mean Discrepancy and modeling degradation as a conditional distribution, achieving superior performance in perceptual quality and no-reference image quality assessment.
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.