Scattering medium brings great difficulties to locate and reconstruct objects especially when the objects are distributed in different positions. In this paper, a novel physics and learning-heuristic method is presented to locate and image the object through a strong scattering medium. A novel physics-informed framework, named DINet, is constructed to predict the depth and the image of the hidden object from the captured speckle pattern. With the phase-space constraint and the efficient network structure, the proposed method enables to locate the object with a depth mean error less than 0.05 mm, and image the object with an average peak signal-to-noise ratio (PSNR) above 24 dB, ranging from 350 mm to 1150 mm. The constructed DINet firstly solves the problem of quantitative locating and imaging via a single speckle pattern in a large depth. Comparing with the traditional methods, it paves the way to the practical applications requiring multi-physics through scattering media.

Color imaging with scattered light is crucial to many practical applications and becomes one of the focuses in optical imaging fields. More physics theories have been introduced in the deep learning (DL) approach for the optical tasks and improve the imaging capability a lot. Here, an efficient color imaging method is proposed in reconstructing complex objects hidden behind unknown opaque scattering layers, which can obtain high reconstruction fidelity in spatial structure and accurate restoration in color information by training with only one diffuser. More information is excavated by utilizing the scattering redundancy and promotes the physics-aware DL approach to reconstruct the color objects hidden behind unknown opaque scattering layers with robust generalization capability by an efficient means. This approach gives impetus to color imaging through dynamic scattering media and provides an enlightening reference for solving complex inverse problems based on physics-aware DL methods.

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