# Robust photon-efficient imaging using a pixel-wise residual shrinkage network

arXiv: https://arxiv.org/abs/2201.01453 · HTML: https://mlsanity.com/paper/2201.01453/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 5 Jan 2022.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [Long-range depth imaging using a single-photon detector array and non-local data fusion](https://mlsanity.com/paper/1812.04943/) (2018)
- [Object Depth Profile and Reflectivity Restoration from Sparse Single-Photon Data Acquired in Underwater Environments](https://mlsanity.com/paper/1608.06143/) (2016)

## Built on by (most cited first, 4 of 4)

- [Deep Domain Adversarial Adaptation for Photon-efficient Imaging](https://mlsanity.com/paper/2201.02475/) (2022)
- [Single-photon Image Super-resolution via Self-supervised Learning](https://mlsanity.com/paper/2303.02033/) (2023)
- [Point upsampling networks for single-photon sensing](https://mlsanity.com/paper/2508.12986/) (2025)
- [SP-TransientBench: A Real-Captured Single Photon Perception Benchmark](https://mlsanity.com/paper/2606.18952/) (2026)

## Abstract (arXiv)

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.