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Deep learning techniques provide a plausible route towards achievingpractical imaging through multimode fibers. However, the results produced bythese methods are often influenced by physical factors like temperature, fiberlength, external perturbations, and polarization state of the input light. Theimpact of other factors, except input light polarization, has been discussed inthe literature for imaging applications. The input polarization has beenconsidered by researchers while looking at the characterization and control ofpolarization in multimode fibers. Here, we show experimentally that the stateof polarization of light, being injected at multimode fiber input, affects thefidelity of reconstructed images from speckle patterns. Certain polarizationstates produce high-quality images at fiber output, while some yield degradedresults. We have designed a conditional generative adversarial network~(CGAN)for image regeneration at various degrees of input light polarization. Wedemonstrate that in the case of multimode fibers that are held fixed, optimalimaging can be achieved by leveraging our CGAN model with the input lightpolarization state, where the fidelity of images is maximum. Our work exhibitshigh average structural similarity index values exceeding 0.9, surpassing thepreviously reported value of 0.8772. We also show that the model can begeneralized to image adequately for all input light polarization states whenthe fiber has bends or twists. We anticipate our work will be a stepping stonetoward developing high-resolution and less invasive multimode fiber endoscopes.

Towards optimal multimode fiber imaging by leveraging input polarization and conditional generative adversarial networks