基于轻量级网络的光纤环图像超分辨率重建北大核心CSCD

为了提高光纤环绕制图像的分辨率并减轻深度学习模型带来的内存和计算开销,提出了一种能够同时提取梯度信息和图像信息的双分支网络,利用轻量级残差块快速轻量的优势来提取网络路径中的图像特征,还引入了多阶段残差特征迁移机制。在梯度信息和特征迁移的共同作用下,网络可以保留丰富的几何结构信息,使重构图像的边缘细节更加清晰。实验结果表明,该模型以较少的参数和0.018 s的运行时间实现了优越的性能,在2^(×)、3^(×)和4^(×)的比例因子下,峰值信噪比分别为44.08 dB、41.35 dB和38.97 dB,结构相似性指数分别为0.985 8、0.979 3和0.976 9,均优于其他现有方法,为后续的光纤环质量检测提供了强有力的保障。

Super-resolution reconstruction of fiber optic coil image based on lightweight network

In order to improve the winding image resolution of the optical fiber coils and reduce the memory and computational overhead caused by the deep learning model, a dual-branch network that can simultaneously extract the gradient information and image information was proposed. The image features in the network path were extracted by using the advantages of speediness and light weight of lightweight residual blocks, and the multi-stage residual feature transfer mechanism was also introduced. Under the combined action of gradient information and feature transfer, the network could retain the rich geometric structure information, which made the edge details of the reconstructed image clearer. The experimental results show that, the proposed model achieves superior performance with fewer parameters and a running time of 0.018 s. Under the double, triple and quadruple scale factors, the peak signal-to-noise ratio is 44.08 dB, 41.35 dB and 38.97 dB, respectively and the structural similarity index is 0.985 8, 0.979 3 and 0.976 9, respectively, which are both superior to other existing methods and provide a strong guarantee for subsequent quality detection of optical fiber coils.