It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with correct and clear details. Existing deep learning works almost neglect the inherent structural information of images, which acts as an important role for visual perception of SR results. In this paper, we design a hierarchical feature exploitation network to probe and preserve structural information in a multi-scale feature fusion manner. First, we propose a cross convolution upon traditional edge detectors to localize and represent edge features. Then, cross convolution blocks (CCBs) are designed with feature normalization and channel attention to consider the inherent correlations of features. Finally, we leverage multi-scale feature fusion group (MFFG) to embed the cross convolution blocks and develop the relations of structural features in different scales hierarchically, invoking a lightweight structure-preserving network named as Cross-SRN. Experimental results demonstrate the Cross-SRN achieves competitive or superior restoration performances against the state-of-the-art methods with accurate and clear structural details. Moreover, we set a criterion to select images with rich structural textures. The proposed Cross-SRN outperforms the state-of-the-art methods on the selected benchmark, which demonstrates that our network has a significant advantage in preserving edges.
翻译:现有的深层学习工作几乎忽略了图像固有的结构信息,而图像的内在结构信息是视觉看待SR结果的一个重要作用。在本文中,我们设计了一个等级特征开发网络,以多尺度特征聚合的方式探测和保存结构信息。首先,我们提议对传统的边缘探测器进行交叉演化,以定位和代表边缘特征。然后,交叉演化区块的设计带有特征正常化,并关注地段的内在关联性。最后,我们利用多尺度特征融合组(MFFG)来嵌入交叉变异区块,并发展不同等级的结构特征关系,同时利用称为Cross-SRN的轻量级结构保护网络。实验结果显示,跨SRN在与最新技术方法的恢复方面实现了竞争或优异性表现,并有准确和明确的结构细节。此外,我们制定了一个标准,用以选择具有丰富结构纹理的图像。拟议的跨尺度组合组合组(MFFG)超越了我们所选择的网络的显著优势。