  Deep learning techniques provide a plausible route towards achieving
practical imaging through multimode fibers. The results produced by these
methods are often influenced by physical factors like temperature, fiber
length, external perturbations, and polarization state of the input light.
Literature focuses on these different elements impacting deep-learning-enabled
multimode imaging, yet the effects of input polarization remain under-explored.
Here, we show experimentally that the state of polarization of light, being
injected at multimode fiber input, affects the fidelity of reconstructed images
from speckle patterns. Certain polarization states produce high-quality images
at fiber output, while some yield degraded results. We have designed a
conditional generative adversarial network~(CGAN) for image regeneration at
various degrees of input light polarization. At a particular polarization state
and with a thinner core multimode fiber, our network can reconstruct images
with an average structural similarity index(SSIM) exceeding 0.9. Hence, in the
case of multimode fibers that are held fixed, optimal imaging can be achieved
by leveraging deep learning models with the input light polarization state,
where the fidelity of images is maximum. We also show that the model can be
trained to image adequately for all input light polarization states when the
fiber has bends or twists. We anticipate that our work will be a stepping stone
toward developing high-resolution and less invasive multimode fiber endoscopes.
