Single image detecting enhancement through scattering media based on transmission matrix with a deep learning network

Abstract Random scattering media that scatters incident light waves for optical diffraction-limit break imaging has important application prospects and significance in biomedical imaging, lithography micromachining and nanomaterial surface shape analysis. However, the transmission matrix, which represents the relationship between incident and output pixels in monochromatic image detecting through scattering media, suffers from severe noise and limited resolution in the reconstructed image owing to its imaging characteristics. In this study, we propose a deep convolution neural network to achieve single image enhancement effects, including denoising and super-resolution on images that are reconstructed by a monochromatic transmission matrix. We demonstrate significantly higher quantitative scores in peak signal-to-noise, SSIM and correlation coefficient compared to conventional methods. We further show that our method is effective for the transmission matrix-based reconstruction operator for both phase conjugation and pseudo-inversion.