To address problems that the loss function does not correlate well with perceptual vision in super-resolution methods based on the convolutional neural network(CNN), a novel model called the ADTV-SRGAN is designed based on the adaptive diagonal total-variation generative adversarial network. Combined with global perception and the local structure adaptive method, spatial loss based on the diagonal variation model is proposed to make the loss function can be adjusted according to the spatial features. Pixel loss and characteristic loss are in combination with the spatial loss for the fusing optimization of the total loss function such that high-frequency details of the images are maintained to improve their quality. The results of experiment show that the proposed method can obtain competitive results in objective evaluations. In subjective assessment, images reconstructed by it are clear, delicate, and natural, and it preserved edge- and texture-related details.

中文翻译：

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用于超分辨率成像的自适应对角全变生成对抗网络

为了解决基于卷积神经网络（CNN）的超分辨率方法中损失函数与感知视觉相关性不好的问题，基于自适应对角全变生成对抗网络设计了一种称为ADTV-SRGAN的新模型. 结合全局感知和局部结构自适应方法，提出了基于对角线变化模型的空间损失，使损失函数可以根据空间特征进行调整。像素损失和特征损失与空间损失相结合，用于对总损失函数进行融合优化，从而保持图像的高频细节以提高其质量。实验结果表明，该方法能够在客观评价中获得有竞争力的结果。