Depth image reconstruction has been of interest in single photon LiDAR. The difficulty of high-accuracy depth image reconstruction, for example, the low-reflection objects are ignored sometimes, results from the signal intensity in the reconstructing, which is heavily affected by the target characteristics. The confidence interval width is utilized to guide the recovery of depth images for achieving high-accuracy depth imaging under the non-negligible differences in target characteristics. This work proposes a confidence interval (CI)-guided depth imaging method, which evaluates the uncertainty of depth estimation with the 95% confidence interval of normal distribution. Three necessary steps exist in process with this CI-guided depth imaging method. Firstly, the noise responses are eliminated using the local gating method. Then, the depth image is reconstructed by a pixel-wise maximum likelihood estimator, and the CI guidance image is calculated from the 95% confidence interval of the mean normal distribution. Finally, the adaptive thresholding segmentation algorithm based on the CI guidance image is adopted to achieve the reconstruction of depth images. The CI-guided depth imaging method presents a unique perspective between signal and noise. This paves the way to improve the depth image recovery accuracy with the state-of-the-art photon-efficient imaging algorithm.
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