Tan Congming, Wang Liejun, Cheng Shuli
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China.
College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China.
Sensors (Basel). 2022 Apr 15;22(8):3058. doi: 10.3390/s22083058.
Recently, the feedforward architecture of a super-resolution network based on deep learning was proposed to learn the representation of a low-resolution (LR) input and the non-linear mapping from these inputs to a high-resolution (HR) output, but this method cannot completely solve the interdependence between LR and HR images. In this paper, we retain the feedforward architecture and introduce residuals to a dual-level; therefore, we propose the dual-level recurrent residual network (DLRRN) to generate an HR image with rich details and satisfactory vision. Compared with feedforward networks that operate at a fixed spatial resolution, the dual-level recurrent residual block (DLRRB) in DLRRN utilizes both LR and HR space information. The circular signals in DLRRB enhance spatial details by the mutual guidance between two directions (LR to HR and HR to LR). Specifically, the LR information of the current layer is generated by the HR and LR information of the previous layer. Then, the HR information of the previous layer and LR information of the current layer jointly generate the HR information of the current layer, and so on. The proposed DLRRN has a strong ability for early reconstruction and can gradually restore the final high-resolution image. An extensive quantitative and qualitative evaluation of the benchmark dataset was carried out, and the experimental results proved that our network achieved good results in terms of network parameters, visual effects and objective performance metrics.
最近，基于深度学习的超分辨率网络的前馈架构被提出来用于学习低分辨率（LR）输入的表示以及从这些输入到高分辨率（HR）输出的非线性映射，但这种方法不能完全解决LR和HR图像之间的相互依存关系。在本文中，我们保留前馈架构并在双层引入残差；因此，我们提出了双层循环残差网络（DLRRN）来生成具有丰富细节和令人满意视觉效果的HR图像。与在固定空间分辨率下运行的前馈网络相比，DLRRN中的双层循环残差块（DLRRB）利用了LR和HR空间信息。DLRRB中的循环信号通过两个方向（从LR到HR和从HR到LR）之间的相互引导来增强空间细节。具体来说，当前层的LR信息由前一层的HR和LR信息生成。然后，前一层的HR信息和当前层的LR信息共同生成当前层的HR信息，依此类推。所提出的DLRRN具有很强的早期重建能力，并且可以逐步恢复最终的高分辨率图像。对基准数据集进行了广泛的定量和定性评估，实验结果证明我们的网络在网络参数、视觉效果和客观性能指标方面都取得了良好的结果。
