High definition images transmission through single multimode fiber using deep learning and simulation speckles

Abstract Multimode fiber (MMF) plays a vital role in promoting the miniaturization of endoscope. However, real-time and high-definition imaging using the MMF that remains a challenging research. Traditional phase compensation and transmission matrix methods are affected by fiber shape and optical devices, which results in low imaging rate and accuracy. Deep learning can be used to construct the inverse transformation matrix (ITM, output to input) of the MMF. However, deep learning requires high similarity between sample sets. In this paper, we combine principal component analysis (PCA) method, deep learning based speckle classification (DLSC) and deep learning based image enhancement (DLIE) to improve imaging definition. To save experimental costs, we use the inverse-PCA method to obtain simulation speckles. The experimental results show that simulation speckles can be used for classification and image reconstruction of experimental speckles. With the difference between simulation and experimental speckles, which brings about low imaging definition. Therefore, we use the DLIE methods to further improve imaging definition. The experimental results show imaging capability with high definition for complex natural scenes, which may provide a feasible method for high definition images transmission through the MMF.