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.