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.

中文翻译：

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通过 CNN 和 TV-TV 最小化实现稳健的单图像超分辨率


单图像超分辨率是提高图像分辨率的过程，从低分辨率（LR）图像获得高分辨率（HR）图像。通过利用大型训练数据集，卷积神经网络 (CNN) 目前在此任务中实现了最先进的性能。然而，在测试/部署期间，它们无法强制 HR 和 LR 图像之间的一致性：如果我们对输出 HR 图像进行下采样，它永远不会与其 LR 输入匹配。基于这一观察，我们建议使用称为 TV-TV 最小化的优化问题对 CNN 输出进行后处理，以增强一致性。正如我们大量的实验所表明的那样，这种后处理不仅在 PSNR 和 SSIM 方面提高了图像质量，而且还使超分辨率任务对算子失配具有鲁棒性，即当真正的下采样算子与实际下采样算子不同时用于创建训练数据集的一个。