Haosen LiuDr. ZHANG Yunping, SherrySherryDr. ZHANG YunpingLam, Edmund Y.Edmund Y.Lam2025-08-272025-08-272024Liu, H., Zhang, Y., & Lam, E. Y. (2024). Photon-limited imaging with quanta image sensors via an unsupervised learning framework. In IEEE (Ed.). 2024 IEEE 34th international workshop on machine learning for signal processing (MLSP). 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP), London, United Kingdom (pp. 1-6). IEEE.979835037225097983503722672161-03711551-2541http://hdl.handle.net/20.500.11861/24674Due to their single-photon sensitivity, quanta image sensors (QIS) are designed to complement traditional image sensors for a wide range of applications in photon-limited imaging conditions. However, the binary nature of QIS data poses compatibility challenges with existing image processing tools, necessitating the development of specialized reconstruction algorithms. While training a deep neural network with paired QIS recordings and corresponding ground truth data in a supervised manner offers superior performance compared to closed-form optimization-based solutions, collecting such a dataset can be laborious or impractical in certain sce-narios. To address this issue, we propose an unsupervised framework that eliminates the reliance on clean ground truth data. Experimental results highlight the superiority of our method over other unsupervised, model-based approaches, particularly in terms of image reconstruction quality. No-tably, our proposed method demonstrates competitiveness with the supervised learning method while circumventing the need for labeled training data.enTrainingImage SensorsSensitivitySupervised LearningTraining DataReconstruction AlgorithmsRecordingImage ReconstructionUnsupervised LearningPhotonicsConference Paper