一种统计约束的单图像超分辨率内点方法
A statistically constrained internal method for single image super-resolution
摘要：
基于深度学习的单图像超分辨率（SR）方法最近引起了很多关注。特别是，各种论文已经表明，学习阶段可以在单个图像上执行，从而导致所谓的内部方法。SinGAN方法就是其中之一，其中图像块的分布是在手头的图像上学习的，并以更精细的尺度传播。现在，有一些情况下，可以假设一些统计先验的最终图像。特别地，许多自然现象产生具有幂律傅立叶谱的图像，诸如云和其他纹理图像。在这项工作中，我们展示了如何将这样的先验信息集成到内部超分辨率方法中，通过限制SinGAN的学习上采样过程。我们考虑各种类型的约束，相关的傅立叶功率谱，颜色直方图和一致性的上采样方案。我们在各种实验中证明，这些约束确实是满足的，但也可以通过所提出的方法来提高一些感知质量的措施。
英文摘要：
Deep learning based methods for single-image super-resolution (SR) have drawn a lot of attention lately. In particular, various papers have shown that the learning stage can be performed on a single image, resulting in the so-called internal approaches. The SinGAN method is one of these contributions, where the distribution of image patches is learnt on the image at hand and propagated at finer scales. Now, there are situations where some statistical a priori can be assumed for the final image. In particular, many natural phenomena yield images having power law Fourier spectrum, such as clouds and other texture images. In this work, we show how such a priori information can be integrated into an internal super-resolution approach, by constraining the learned up-sampling procedure of SinGAN. We consider various types of constraints, related to the Fourier power spectrum, the color histograms and the consistency of the upsampling scheme. We demonstrate on various experiments that these constraints are indeed satisfied, but also that some perceptual quality measures can be improved by the proposed approach.
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