Some image restoration tasks like demosaicing require difficult training samples to learn effective models. Existing methods attempt to address this data training problem by manually collecting a new training dataset that contains adequate hard samples, however, there are still hard and simple areas even within one single image. In this paper, we present a data-driven approach called PatchNet that learns to select the most useful patches from an image to construct a new training set instead of manual or random selection. We show that our simple idea automatically selects informative samples out from a large-scale dataset, leading to a surprising 2.35dB generalisation gain in terms of PSNR. In addition to its remarkable effectiveness, PatchNet is also resource-friendly as it is applied only during training and therefore does not require any additional computational cost during inference.

中文翻译：

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学习从图像中采样最有用的培训补丁

一些图像恢复任务（例如去马赛克）需要困难的训练样本才能学习有效的模型。现有方法试图通过手动收集包含足够的硬样本的新训练数据集来解决此数据训练问题，但是，即使在单个图像中，仍然存在硬区域和简单区域。在本文中，我们提出了一种称为PatchNet的数据驱动方法，该方法学会从图像中选择最有用的补丁，以构造新的训练集，而不是手动或随机选择。我们表明，我们的简单想法会自动从大规模数据集中选择信息量丰富的样本，从而在PSNR方面带来令人惊讶的2.35dB泛化增益。除了显著成效外，