基于自适应级联的注意力网络的超分辨率重建

深度卷积神经网络显著提升了单图像超分辨率的性能. 通常, 网络越深, 性能越好. 然而加深网络往往会急剧增加参数量和计算负荷, 限制了在资源受限的移动设备上的应用. 提出一个基于轻量级自适应级联的注意力网络的单图像超分辨率方法. 特别地提出了局部像素级注意力模块, 给输入特征的每一个特征通道上的像素点都赋以不同的权值, 从而为重建高质量图像选取更精确的高频信息. 此外, 设计了自适应的级联残差连接, 可以自适应地结合网络产生的层次特征, 能够更好地进行特征重用. 最后, 为了充分利用网络产生的信息, 提出了多尺度全局自适应重建模块. 多尺度全局自适应重建模块使用不同大小的卷积核处理网络在不同深度处产生的信息, 提高了重建质量. 与当前最好的类似方法相比, 该方法的参数量更小, 客观和主观度量显著更好.

Adaptive Attention Network for Image Super-resolution

Deep convolutional neural networks have significantly improved the performance of single image super-resolution. Generally, the deeper the network, the better the performance. However, deepening network often increases the number of parameters and computational cost, which limits its application on resource constrained mobile devices. In this paper, we propose a single image super-resolution method based on a lightweight adaptive cascading attention network. In particular, we propose a local pixel-wise attention block, which assigns different weights to pixels on each channel, so as to select high-frequency information for reconstructing high quality image more accurately. In addition, we design an adaptive cascading residual connection, which can adaptively combine hierarchical features and is propitious to reuse feature. Finally, in order to make full use of all hierarchical features, we propose a multi-scale global adaptive reconstruction block. Multi-scale global adaptive reconstruction block uses convolution kernels of different sizes to process different hierarchical features, hence can reconstruct high-resolution image more effectively. Compared with other state-of-the-art methods, our method has fewer parameters and achieves superior performance.