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Edge detection in single multimode fiber imaging based on deep learning.

We propose a new edge detection scheme based on deep learning in single multimode fiber imaging. In this scheme, we creatively design a novel neural network, whose input is a one-dimensional light intensity sequence, and the output is the edge detection result of the target. Different from the traditional scheme, we can directly obtain the edge information of unknown objects by using this neural network without rebuilding the image. Simulation and experimental results show that, compared with the traditional method, this method can get better edge details, especially in the case of low sampling rates. It can increase the structural similarity index of edge detection imaging from 0.38 to 0.62 at the sampling rate of 0.6%. At the same time, the robustness of the method to fiber bending is also proved. This scheme improves the edge detection performance of endoscopic images and provides a promising way for the practical application of multimode fiber endoscopy.