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.

中文翻译：

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具有高质量优先级和降级学习的感知图像恢复

感知图像恢复寻求最有可能降级为给定图像的高保真图像。为了获得更好的视觉质量，先前的工作建议通过利用生成模型的潜在空间在自然图像流形内寻找解决方案。但是，仅当潜在嵌入接近先前的分布时，才能保证生成图像的质量。在这项工作中，我们建议限制先验歧管内的可行区域。这是通过针对两个分布的非参数度量来完成的：最大平均差异（MMD）。此外，我们将退化过程直接建模为条件分布。我们证明了我们的模型在测量还原图像和降级图像之间的相似性方面表现良好。与其在退化的图像上优化长而受批评的像素方向距离，不如说是 我们依靠这种模型来找到视觉愉悦的图像的可能性很高。我们的同时恢复和增强框架很好地概括了现实世界中复杂的降级类型。感知质量和无参考图像质量评估（NR-IQA）的实验结果证明了我们方法的优越性能。