Single-photon light detection and ranging (LiDAR) has been widely applied to 3D imaging in challenging scenarios. However, limited signal photon counts and high noises in the collected data have posed great challenges for predicting the depth image precisely. In this paper, we propose a pixel-wise residual shrinkage network for photon-efficient imaging from high-noise data, which adaptively generates the optimal thresholds for each pixel and denoises the intermediate features by soft thresholding. Besides, redefining the optimization target as pixel-wise classification provides a sharp advantage in producing confident and accurate depth estimation when compared with existing research. Comprehensive experiments conducted on both simulated and real-world datasets demonstrate that the proposed model outperforms the state-of-the-arts and maintains robust imaging performance under different signal-to-noise ratios including the extreme case of 1:100.
Deep learning is emerging as an important tool for single-photon light detection and ranging (LiDAR) with high photon efficiency and image reconstruction quality. Nevertheless, the existing deep learning methods still suffer from high memory footprint and low inference speed, which undermine their compatibility when it comes to dynamic and long-range imaging with resource-constrained devices. By exploiting the sparsity of the data, we proposed an efficient neural network architecture which significantly reduces the storage and computation overhead by skipping the inactive sites with no photon counts. In contrast with the state-of-the-art deep learning methods, our method supports one-shot processing of data frames with high spatial resolution, and achieves over 90% acceleration in computation speed without sacrificing the reconstruction quality. In addition, the speed of our method is not sensitive to the detection distance. The experiment results on public real-world dataset and our home-built system have demonstrated the outstanding dynamic imaging capability of the algorithm, which is orders of magnitude faster than the competing methods and does not require any data pruning for hardware compatibility.
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