基于自相似特征增强网络结构的图像超分辨率重建

Self-similarity enhancement network for image super-resolution

深度卷积神经网络最近在图像超分辨率方面展示了高质量的恢复效果。然而,现有的图像超分辨率方法大多只考虑如何充分利用训练集中固有的静态特性,却忽视了低分辨率图像本身的自相似特征。为了解决这些问题,本文设计了一种自相似特征增强的网络结构(SSEN)。具体来说,本文将可变形卷积嵌入到金字塔结构中并结合跨层次协同注意力,设计出了一个能够充分挖掘多层次自相似特征的模块,即跨层次特征增强模块。此外,本文还在堆叠的密集残差块中引入池化注意力机制,利用条状池化扩大卷积神经网络的感受野并在深层特征中建立远程依赖关系,从而深层特征中相似度较高的部分能够相互补充。在常用的五个基准测试集上进行了大量实验,结果表明,SSEN比现有的方法在重建效果上具有明显提升。

Deep convolutional neural networks(DCNN)recently demonstrated high-quality restoration in the single image super-resolution(SISR).However,most of the existing image super-resolution methods only consider making full use of the inherent static characteristics of the training sets,ignoring the internal self-similarity of lowresolution images.In this paper,a self-similarity enhancement network(SSEN)is proposed to address abovementioned problems.Specifically,we embedded the deformable convolution into the pyramid structure and combined it with the cross-level co-attention to design a module that can fully mine multi-level self-similarity,namely the cross-level feature enhancement module.In addition,we introduce a pooling attention mechanism into the stacked residual dense blocks,which uses a strip pooling to expand the receptive field of the convolutional neural network and establish remote dependencies within the deep features,so that the patches with high similarity in deep features can complement each other.Extensive experiments on five benchmark datasets have shown that the SSEN has a significant improvement in reconstruction effect compared with the existing methods.