论文标题
使用像素的残留收缩网络使用稳健的光子有效成像
Robust photon-efficient imaging using a pixel-wise residual shrinkage network
论文作者
论文摘要
单光子光检测和射程（LIDAR）已在具有挑战性的情况下广泛应用于3D成像。但是，收集的数据中有限的信号光子计数和高噪声提出了精确预测深度图像的巨大挑战。在本文中，我们提出了一个通过高噪声数据的光子效率成像的像素的残留收缩网络，该网络可自适应地生成每个像素的最佳阈值，并通过软阈值来降低中间特征。此外，与现有研究相比，将优化目标重新定义为Pixel分类，在产生自信和准确的深度估计方面具有巨大的优势。在模拟和现实世界数据集上进行的全面实验表明，所提出的模型在不同的信噪比之比（包括1：100的极端情况）下优于最先进的模型，并保持稳健的成像性能。
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.
