Single image super-resolution (SISR) is a traditional image restoration problem. Given an image with low resolution (LR), the task of SISR is to find the homologous high-resolution (HR) image. As an ill-posed problem, there are works for SISR problem from different points of view. Recently, deep learning has shown its amazing performance in different image processing tasks. There are works for image super-resolution based on convolutional neural network (CNN). In this paper, we propose an adaptive residual channel attention network for image super-resolution. We first analyze the limitation of residual connection structure and propose an adaptive design for suitable feature fusion. Besides the adaptive connection, channel attention is proposed to adjust the importance distribution among different channels. A novel adaptive residual channel attention block (ARCB) is proposed in this paper with channel attention and adaptive connection. Then, a simple but effective upscale block design is proposed for different scales. We build our adaptive residual channel attention network (ARCN) with proposed ARCBs and upscale block. Experimental results show that our network could not only achieve better PSNR/SSIM performances on several testing benchmarks but also recover structural textures more effectively.

中文翻译：

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单幅图像超分辨率自适应残差通道注意网络

单幅图像超分辨率（SISR）是传统的图像恢复问题。给定低分辨率 (LR) 图像，SISR 的任务是找到同源的高分辨率 (HR) 图像。作为不适定问题，SISR 问题从不同的角度都有工作。最近，深度学习在不同的图像处理任务中表现出了惊人的性能。有基于卷积神经网络 (CNN) 的图像超分辨率工作。在本文中，我们提出了一种用于图像超分辨率的自适应残差通道注意网络。我们首先分析了残差连接结构的局限性，并提出了一种适合特征融合的自适应设计。除了自适应连接之外，还提出了通道注意力来调整不同通道之间的重要性分布。本文提出了一种具有通道注意力和自适应连接的新型自适应残差通道注意力块（ARCB）。然后，针对不同规模提出了一种简单但有效的高档块设计。我们使用建议的 ARCB 和高档块构建我们的自适应残差通道注意网络 (ARCN)。实验结果表明，我们的网络不仅可以在多个测试基准上获得更好的 PSNR/SSIM 性能，而且可以更有效地恢复结构纹理。