Seeing through multimode fibres using real-valued intensity transmission matrix with deep learning

Multimode fibers (MMFs) are emerging as a highly attractive technology for applications in biomedical endoscopy and telecommunications, thanks to their ability to transmit images and data through a large number of transverse optical modes. However, light transmission through MMFs suffers from distortions caused by mode dispersion and coupling. While recent deep learning advancements have shown potential for improving image transmission through MMFs, these methods typically require an extensive training dataset and often exhibit limited generalization capability. In this work, we propose a hybrid approach that combines a real-valued intensity transmission matrix (RVITM) with deep learning for enhanced image retrieval through MMFs. Our approach first characterizes the MMF and retrieves images using a RVITM algorithm, followed by refinement with a hierarchical, parallel multi-scale (HPM)-attention U-Net to improve image quality. Experimental results demonstrated that our approach achieved high-quality reconstructions, with structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) values of up to 0.9524 and 33.244 dB, respectively. This approach also offers strong generalization capabilities, requires fewer training samples and converges more quickly compared to purely deep learning-based methods reported in the literature. These results highlight the potential of our method for ultrathin endoscopy applications and spatial-mode multiplexing in telecommunications using MMFs.