Learning to See Speckle in the Weak Laser Field through Multimode Fiber
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Abstract:
Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However, most studies focus on designing complex network architectures to improve reconstruction, but these network models struggle to reconstruct images in a weak laser field. In the paper, a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover, the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed, which enhances the robustness of the model.
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Project Supported:
Opening Foundation of Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems (2019LODTS004)
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