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Deep Learning for Disturbance-Resistant Compressive Sensing Multimode Fiber Imaging
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DOI：10.1002/lpor.202502398.png)
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Abstract
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Multimode fiber (MMF) is promising for minimally invasive endoscopy due to its ultra-thin, non-invasive nature. However, existing modulation-based point-scanning methods are fundamentally restricted by the intrinsic speed limits of physical modulation hardware, precluding high frame rate imaging, while compressed sensing (CS) requires a stable measurement matrix, which is difficult to maintain in MMF due to its dynamic scattering property. Here, we introduce a Physics-integrated Disturbance-Resistant Reconstruction Network (PDRNet). It addresses transmission matrix mismatch in CS-MMF systems by leveraging physics priors learned from disturbed experimental data, allowing CS reconstruction at a maximum of 300 frames per second. PDRNet achieves a maximum 97.6% improvement in structural similarity (SSIM) at a 1.5% compression ratio compared to conventional CS methods on the H&E-stained dataset. We validated these advantages on fluorescent beads, H&E-stained tissue slices (animal/plant), and ex vivo animal tissues. By overcoming the fundamental challenge of instability, PDRNet provides MMF endoscopy with the potential for high-speed imaging.
Keywords:
compressed sensing
deep learning
disturbance resistant
multimode fiber
physics integrated
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