Learning-based lensless imaging through optically thick scattering media

Abstract

The problem of imaging through thick scattering media is encountered in many disciplines of science, ranging from mesoscopic physics to astronomy. Photons become diffusive after propagating through a scattering medium with an optical thickness of over 10 times the scattering mean free path. As a result, no image but only noise-like patterns can be directly formed. We propose a hybrid neural network for computational imaging through such thick scattering media, demonstrating the reconstruction of image information from various targets hidden behind a white polystyrene slab of 3 mm in thickness or 13.4 times the scattering mean free path. We also demonstrate that the target image can be retrieved with acceptable quality from a very small fraction of its scattered pattern, suggesting that the speckle pattern produced in this way is highly redundant. This leads to a profound question of how the information of the target being encoded into the speckle is to be addressed in future studies.