3D localization of point source is widely used in many fields, such as bioimaging and autonomous driving fields. However, the localization is hard to perform under scattering conditions because of the diffuse effect of the scattering. We propose a novel method for 3D localization of point source under scattering conditions based on light field imaging and deep learning by only one shot. First, we introduce the description of the point source in a light field wise and how to localize a point source by its light field. On the basis, we elaborate on the effect of scattering on a light field and how to retrieve the location of a point source from a light field with scattering. Then, the effect of aberration on a light field will be introduced. We also build an artificial scene and a deep learning framework to perform a 3D localization practically, and the feasibility and accuracy of our method have been evaluated.
Imaging through scattering layers plays an important role in the field of optical imaging. Because of its characteristics, we can observe some targets that are invisible or unobservable. Now, it is a simple and effective way to process images of scattering layers by autocorrelation. However, due to the memory effect and the limitation of the acquisition environment, imaging through scattering layers still lacks the ability to accurately detect unknown objects. In this paper, we analyzed the influence of memory effects and actual acquisition environment on speckle correlations imaging. By controlling the various variables of the experimental device and the image processing, different experimental images and restoration results of the images are obtained. The memory effects control the optical thickness of the scattering layer, the size of the target, and the distance from the target to the scattering layer. There must be appropriate experimental parameter settings to meet the memory effect requirements. In addition, the selection of the position of the image acquisition device determines the degree of dispersion of the speckle. Image processing is mainly for the filtering of space domain and frequency domain, and for changes in constraints in Hybrid Input-Output algorithms. Finally, comparing the influence of all the parameters on the final restored image, the reasonable acquisition scheme and image processing scheme for different targets and scattering media can be obtained. It has reference and guiding significance for the application of imaging through scattering layers via speckle correlations.