Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications. With coherent illumination, the light scattered from diffusive walls forms speckle patterns that contain information of the hidden object. It is possible to realize non-line-of-sight (NLOS) recognition with these speckle patterns. We introduce a novel approach based on speckle pattern recognition with deep neural network, which is simpler and more robust than other NLOS recognition methods. Simulations and experiments are performed to verify the feasibility and performance of this approach.
Imaging via speckle correlation is another method that was recently developed. When a rough surface is illuminated by a coherent light ( , a laser beam), a speckle pattern is observed in the image plane. The key principle of this method is that the auto-correlation of the speckle pattern is essentially identical to the original object's auto-correlation, as if it is imaged by a perfect diffraction-limited optical system that has replaced the scattering medium. Consequently, the object's image can be obtained from its auto-correlation by an iterative phase retrieval algorithm @cite_21 . In particular, for seeing without line-of-sight, the light back-scattered from a diffusive wall is used to image the hidden objects.
@cite_21
2000443157
Diffraction-limited imaging in a variety of complex media is realized based on analysis of speckle correlations in light captured using a camera phone.