Single-Pixel Imaging

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8 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Bayesian Sequential Optimal Experimental Design for Single-Pixel Imaging using Reinforcement Learning
Nov 2, 2026Marcos Antonio Obando, Tianyuan Wang, Tanja Tarvainen +2Single-Pixel Imaging Adaptive Sensing
Learning to read residual images for single-pixel photography
Nov 1, 2026Hongjun An, Shuaizhe An, Tong Tian +1Single-Pixel Imaging Image Reconstruction
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, 710072, China. · Institute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich Schiller University, Jena, 07743, Germany. · Helmholtz Institute Jena, Jena, 07743, Germany. +1
Learned optical compression for single-pixel quantitative phase imaging
Nov 1, 2026Ary Portes, Yuntian Wang, Che-Yung Shen +2Single-Pixel Imaging Computational Imaging
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA · Bioengineering Department, University of California, Los Angeles, CA, 90095, USA · California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
Computational compressive imaging reconstruction with a simulated-annealing-based Ising solver
Oct 14, 2026Ze Zheng, Xiaoyan Wu, Hang Xu +3Single-Pixel Imaging Image Reconstruction
Institute for Quantum Sensing and Information Processing, State Key Laboratory of Photonics and Communications, Shanghai Jiao Tong University, Shanghai 200240, China · Hefei National Laboratory, Hefei 230088, China · Shanghai Research Center for Quantum Sciences, Shanghai 201315, China
The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing
Jul 24, 2026Yuyuan Han, Jingwei Li, Xiaoxia Zhang +9Image Segmentation Representation Learning
Electronic Materials Research Laboratory, Key Laboratory of the Ministry of Education and International Center for Dielectric Research, School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China · Micius Laboratory, Henan Academy of Sciences, Zhengzhou 450052, China · Department of Pharmacy, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, China +1
Compressive sensing inspired self-supervised single-pixel imaging
Mar 31, 2026Jijun Lu, Yifan Chen, Libang Chen +5Single-Pixel Imaging Image Reconstruction
Institute of Artificial Intelligence (TeleAI), China Telecom, China · School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai, China · College of Future Information Technology, Fudan University, Shanghai, China +3
Compressive single-pixel imaging via a wavelength-multiplexed spatially incoherent diffractive optical processor
Mar 23, 2026Xiao Wang, Yiyang Wu, Yuntian Wang +8Single-Pixel Imaging Computational Imaging
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA · Bioengineering Department, University of California, Los Angeles, CA, 90095, USA · California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
Single Pixel Image Classification using an Ultrafast Digital Light Projector
Mar 12, 2026Aisha Kanwal, Graeme E. Johnstone, Fahimeh Dehkhoda +5Single-Pixel Imaging Image Classification
Institute of Photonics, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK · School of Engineering, University of Edinburgh, Sanderson Building, Robert Stevenson Road, Edinburgh, EH9 3FB, UK

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Bayesian Sequential Optimal Experimental Design for Single-Pixel Imaging using Reinforcement Learning

Marcos Antonio Obando, Tianyuan Wang, Tanja Tarvainen, Tristan van Leeuwen+1

Single-Pixel Imaging Adaptive Sensing Bayesian Experimental Design Computational Imaging Compressed Sensing Bayesian RL

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Adaptive compressed sensing (CS) aims to select the most informative measurements sequentially, which is crucial for many modern imaging systems. This task can be formulated as a Bayesian sequential optimal experimental design (sOED) problem, where each new acquisition is chosen to maximally reduce uncertainty about the unknown image given prior information and subsequent data provided by acquisitions. However, in single-pixel imaging (SPI), the large design space consists of all possible binary illumination patterns. Directly optimizing the Bayesian sOED objective over this space using conventional methods is generally challenging. In this work, we reformulate sOED for SPI as a reinforcement learning (RL) problem. In RL, an agent learns a policy through repeated experimental trials to maximize a reward, here given as the expected information gain, the most complete measure of uncertainty reduction in Bayesian OED. This avoids the need to directly optimize complex information-theoretic objectives during a measurement to obtain the next design. Instead, the result is a learned policy function that directly maps the current posterior belief about the image to the next optimal binary measurement pattern, in a computationally efficient way. Using a simple yet representative SPI imaging scenario, this work evaluates the scalability of the learned policy under two different policy parameterizations. Furthermore, we investigate how performance deteriorates when only approximate posterior information is available or the measurements are impaired by noise, and propose several strategies to introduce additional design constraints motivated by biomedical applications. The numerical experiments demonstrate that the proposed Bayesian sOED-RL framework remains robust and can be extended to more complex, large-scale SPI scenarios.