Nested Dense Attention Network for Single Image Super-Resolution

Abstract

Recently, deep convolutional neural networks (CNNs) are widely used in single image super-resolution (SISR) and have recorded impressive performance. However, most of the existing CNNs architectures can not fully utilize the correlation of feature maps in the middle layers, and abundant features of different levels are lost. Furthermore, convolution operation is limited by processing one local neighborhood at a time, which lacks global information. To address these issues, we propose the nested dense attention network (NDAN) for generating more refined and structured high-resolution images. Specifically, we propose nested dense structure (NDS) to better integrate features of different levels extracted from different layers. Besides that, in order to capture inter-channel dependencies more efficiently, we propose the adaptive channel attention module (ACAM) to adaptively rescale channel-wise features by automatically adjusting the weights of different receptive fields. Furthermore, to better explore the global-level context information, we design hybrid non-local module (HNLM) and hybrid non-local up-sampler (HNLU) to upscale the images by capturing spatial-wise long-distance dependencies and channel-wise long-distance correlation. Numerous experiments demonstrate the effectiveness of our model by achieving higher PSNR and SSIM scores and generating images with better structures against the state-of-the-art methods.