Reinforcement Learning Based Tuning-free Plug-and-Play Image Reconstruction Method for Single Photon Imaging

CHEN Shuang; TIAN Ye; FU Ying

doi:10.12263/DZXB.20230343

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Reinforcement Learning Based Tuning-free Plug-and-Play Image Reconstruction Method for Single Photon Imaging

PAPERS | 更新时间：2026-05-07

- Reinforcement Learning Based Tuning-free Plug-and-Play Image Reconstruction Method for Single Photon Imaging
- ACTA ELECTRONICA SINICA Vol. 52, Issue 10, Pages: 3600-3612(2024)
- 作者机构：
  
  1.北京理工大学计算机学院，北京 100081
  2.北京理工大学信息与电子学院，北京 100081
  3.北京理工大学复杂环境智能感测技术工信部重点实验室，北京 100081
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62331006;62171038;62088101);Fundamental Research Funds for the Central Universities
- DOI：10.12263/DZXB.20230343
  CLC： TP391;
- Received：18 April 2023，
  
  Revised：2024-05-29，
  
  Published：25 October 2024
- 稿件说明：
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陈爽, 田烨, 付莹. 基于强化学习的免调参即插即用单光子图像重建方法[J]. 电子学报, 2024, 52(10): 3600-3612.

CHEN Shuang, TIAN Ye, FU Ying. Reinforcement Learning Based Tuning-free Plug-and-Play Image Reconstruction Method for Single Photon Imaging[J]. Acta Electronica Sinica, 2024, 52(10): 3600-3612.
陈爽, 田烨, 付莹. 基于强化学习的免调参即插即用单光子图像重建方法[J]. 电子学报, 2024, 52(10): 3600-3612. DOI：10.12263/DZXB.20230343

CHEN Shuang, TIAN Ye, FU Ying. Reinforcement Learning Based Tuning-free Plug-and-Play Image Reconstruction Method for Single Photon Imaging[J]. Acta Electronica Sinica, 2024, 52(10): 3600-3612. DOI：10.12263/DZXB.20230343

摘要

量子图像传感器（Quanta Image Sensor，QIS）具有超高的单光子灵敏度与空间分辨率，因此其有望成为替代CMOS的下一代图像传感器.然而，从QIS获取的原始数据是二进制的图像帧，因此需要采用图像重建算法从二进制观测值中恢复原始场景.现有的方法包括基于模型的QIS图像重建和基于深度学习的QIS图像重建，其中基于模型的QIS图像重建通常需要进行大量的优化迭代，且对内部参数选择高度敏感.而基于深度学习的QIS图像重建则往往需要对每个具有细微不同的任务设计和训练单独的网络模型，灵活性欠缺并在很大程度上限制了其实用性.为了解决上述QIS图像重建方法的局限性，本文提出一种基于强化学习的免调参即插即用单光子图像重建方法.该方

法能够自适应地对不同输入图像、不同过采样倍率和重建所处的不同迭代轮次动态选取合适的参数，实现更好的重建效果.具体地，本文将即插即用框架下的QIS图像重建任务中需要手动调整的参数建模为序列决策问题，引入结合无模型强化学习和基于模型强化学习思想的算法学习一个最佳策略，以实现对于不同输入图像在每次优化迭代时的最优超参选择.在合成和真实数据集上的实验结果表明，本文提出的方法可以在过采样倍率为4、6、8三种情况下，在峰值信噪比上相较现有先进方法提高约0.44~0.60 dB，在重建的视觉效果上也能够保留更多的纹理细节.真实极暗光QIS图像数据已在

https：//github.com/ying-fu/Real-SPAD-Dataset

https://github.com/ying-fu/Real-SPAD-Dataset

公开.

Abstract

Quantum image sensor (QIS) has ultra-high single-photon sensitivity and spatial resolution

making it a promising alternative to CMOS image sensor (CIS) as the next-generation image sensor. However

image reconstruction of QIS differs from traditional image reconstruction methods

it aims to recover the original scene from binary measurements. The existing methods include model-based QIS image reconstruction and deep learning-based QIS image reconstruction. Model-based methods are largely based on optimization and are highly sensitive to the selection of hyperparameters. While deep learning-based methods require designing and training separate models for QIS image reconstruction tasks with slight variations in detail

which is inflexible and limits its usefulness to a large extent. In order to tackle the problems in QIS image reconstruction

a tuning-free plug-and-play alternating direction method of multiplier (TFPnP-ADMM) QIS image reconstruction method is proposed in this paper

which can adaptively select appropriate parameters dynamically for different input images with various oversampling factors

so as to achieve better image reconstruction performance. Specifically

in this paper

the parameters that need to be manually tuned in the QIS image reconstruction process under the plug-and-play (PnP) framework are modeled as a sequential decision problem

and a mixed model-free and model-based reinforcement learning algorithm is introduced to learn an optimal strategy

which could determine optimal hyperparameters at each itera

tion for different input images. The experimental results on synthetic dataset and real dataset demonstrate that

compared with existing state-of-the-art methods

the proposed method improves the peak signal-to-noise ratio by approximately 0.44~0.60 dB under oversampling rates of 4

and 8. Furthermore

the visual results demonstrate the superiority of the proposed method in retaining more texture details. Real extremely low light QIS image data is available at

https://github.com/ying-fu/Real-SPAD-Dataset

关键词

Keywords

references

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