Abstract
How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models. Existing methods mostly take a set of high-resolution (HR) images as GTs and apply various degradations to simulate their low-resolution (LR) counterparts. Though great progress has been achieved, such an LR-HR pair generation scheme has several limitations. First, the perceptual quality of HR images may not be high enough, limiting the quality of Real-ISR outputs. Second, existing schemes do not consider much human perception in GT generation, and the trained models tend to produce over-smoothed results or unpleasant artifacts. With the above considerations, we propose a human guided GT generation scheme. We first elaborately train multiple image enhancement models to improve the perceptual quality of HR images, and enable one LR image having multiple HR counterparts. Human subjects are then involved to annotate the high quality regions among the enhanced HR images as GTs, and label the regions with unpleasant artifacts as negative samples. A human guided GT image dataset with both positive and negative samples is then constructed, and a loss function is proposed to train the Real-ISR models. Experiments show that the Real-ISR models trained on our dataset can produce perceptually more realistic results with less artifacts. Dataset and codes can be found at this https URL
Abstract (translated)
生成真相图像(GT)是训练真实图像超分辨率(Real-ISR)模型的一个关键问题。现有的方法大多将高分辨率(HR)图像作为真相图像，并应用各种退化来模拟其低分辨率(LR)对应物。尽管取得了很大进展，但这种LR-HR对偶生成方法有几个限制。首先，HR图像的感知质量可能不够高，限制Real-ISR输出质量。其次，现有的生成方法并未充分考虑人类感知在真相图像生成中的作用，训练模型往往产生过度平滑的结果或不愉快的痕迹。基于以上考虑，我们提出了一种人类引导的真相图像生成方法。我们首先 elaborately 训练多个图像增强模型，以提高HR图像的感知质量，并使一个LR图像具有多个HR对应物。人类 subjects then 参与标注增强后的HR图像中的高质量区域，并将其作为真相图像，并将不愉快的痕迹区域作为负样本。一个包含正负样本的人类引导的真相图像数据集随后构建，并提出了损失函数来训练Real-ISR模型。实验表明，在我们数据集中训练的Real-ISR模型可以产生感知上更为真实，减少痕迹的结果。数据集和代码可在此https URL中找到。
URL
https://arxiv.org/abs/2303.13069
