论文标题
通过CNN和TV-TV最小化，可靠的单片图像超分辨率
Robust Single-Image Super-Resolution via CNNs and TV-TV Minimization
论文作者
论文摘要
单片图像超分辨率是增加图像分辨率的过程，从低分辨率（LR）一个获得高分辨率（HR）图像。通过利用大型培训数据集，卷积神经网络（CNN）目前在此任务中实现最先进的性能。但是，在测试/部署期间，它们无法在HR和LR图像之间执行一致性：如果我们将输出hr图像下样本，则它永远不会匹配其LR输入。基于此观察结果，我们建议使用一个优化问题进行后处理CNN输出，我们称之为TV-TV最小化，从而实现一致性。正如我们的广泛实验所表明的那样，这种后处理不仅可以根据PSNR和SSIM来提高图像的质量，而且还使超级分辨率的任务与操作员不匹配的稳健任务（即，当真实的下采样操作员都不同于用于创建训练数据集的一种。
Single-image super-resolution is the process of increasing the resolution of an image, obtaining a high-resolution (HR) image from a low-resolution (LR) one. By leveraging large training datasets, convolutional neural networks (CNNs) currently achieve the state-of-the-art performance in this task. Yet, during testing/deployment, they fail to enforce consistency between the HR and LR images: if we downsample the output HR image, it never matches its LR input. Based on this observation, we propose to post-process the CNN outputs with an optimization problem that we call TV-TV minimization, which enforces consistency. As our extensive experiments show, such post-processing not only improves the quality of the images, in terms of PSNR and SSIM, but also makes the super-resolution task robust to operator mismatch, i.e., when the true downsampling operator is different from the one used to create the training dataset.
