Recently, deep convolutional neural networks have demonstrated high-quality reconstruction for single image super-resolution. In this study, we present a network by using residual blocks with cascading simple blocks to improve the image resolution. Cascading simple blocks with a multi-layer perceptron are conducive to extract features and approximate a complex mapping with fewer parameters. Skip connections can help to alleviate the vanishing-gradient problem of deep networks. In addition, our network contains two pathways. One is to predict the high frequency information of the high resolution image and the other is to predict the low frequency information of the high resolution image. Then the information of two pathways is fused, and pixel-shuffle is used for upsampling. Moreover, to capture texture details of images, we introduce a novel loss function called detail perception loss, which is used to measure the difference of the wavelet coefficients from the reconstructed image and ground truth. By reducing detail perception loss, texture details of the reconstructed image are becoming more similar with texture details of ground truth. Extensive quantitative and qualitative experiments on four benchmark datasets show that our method achieves superior performance over typical single image super-resolution methods.

最近，深度卷积神经网络已经证明了单图像超分辨率的高质量重建。在这项研究中，我们提出了一种通过使用残差块和级联的简单块来提高图像分辨率的网络。将具有多层感知器的简单块级联有助于提取特征并使用较少的参数来近似复杂的映射。跳过连接可以帮助缓解深度网络消失的梯度问题。此外，我们的网络包含两个途径。一种是预测高分辨率图像的高频信息，另一种是预测高分辨率图像的低频信息。然后将两个路径的信息融合在一起，并使用像素混洗进行上采样。此外，要捕获图像的纹理细节，我们介绍了一种称为细节感知损失的新颖损失函数，该函数用于测量重构图像与地面真实情况之间的小波系数差异。通过减少细节感知损失，重构图像的纹理细节与地面真相的纹理细节变得越来越相似。在四个基准数据集上进行的大量定量和定性实验表明，我们的方法比典型的单幅图像超分辨率方法具有更高的性能。