Lightweight spad neural network reconstruction method with depth-intensity joint optimization

Abstract

The application discloses a kind of depth-intensity joint optimization light weight SPAD neural network reconstruction methods, belong to single-photon imaging and depth learning technical field, including: based on wavelet transform to the original histogram data of SPAD array is carried out multistage wavelet decomposition, obtain compressed time-frequency domain data;Utilize multiscale superpixel to carry out non-maximum suppression, and obtain estimated depth map by voting mechanism;Local histogram is extracted from compressed time-frequency domain data, and sum along time dimension obtains intensity map;The estimated depth map is expanded, and expanded depth map is obtained;The double-branch complementary depth reconstruction network including depth branch, edge branch and perception interaction module is constructed;Real depth map and real edge gradient map are used to guide network, and training is carried out using joint loss function, to realize light weight SPAD neural network reconstruction.The method significantly improves the depth reconstruction accuracy under low illumination while greatly reducing the amount of calculation.

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