Single-shot enhanced imaging through multimode fiber based on self-learning networks

Abstract

High-fidelity imaging through a multimode fiber (MMF) plays a crucial role in various fields such as medicine, communication, and detection. However, the optical transmission matrix of the MMF suffers from dimensionality reduction. This case results in poor reconstruction quality such as low resolution and noise. In this paper, an unsupervised self-learning circulate learning network is employed to enhance a single degraded image without ground truth images. Moreover, an edge-preserving smoothing filter is applied to address the heavy noise problem of the reconstructed images. Experimental results demonstrate that the proposed method can improve the dimensionality and fidelity of the reconstructed target. Compared to traditional transmission matrix-based reconstruction methods, we have a competitive advantage in terms of evaluation metrics. The proposed method further advances the development of imaging through a multimode fiber.