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作 者:Yiming Liu Xuri Yao Tao Zhang Yifei Sun Ying Fu
机构地区:[1]School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China [2]Center for Quantum Technology Research and Key Laboratory of Advanced Optoelec-tronic Quantum Architecture and Measurements(MOE),School of Physics,Beijing Institute of Technology,Beijing 100081,China [3]School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China [4]Uniview Technology,Hangzhou 310056,China
出 处:《Journal of Beijing Institute of Technology》2025年第1期1-17,共17页北京理工大学学报(英文版)
摘 要:Single-photon sensors are novel devices with extremely high single-photon sensitivity and temporal resolution.However,these advantages also make them highly susceptible to noise.Moreover,single-photon cameras face severe quantization as low as 1 bit/frame.These factors make it a daunting task to recover high-quality scene information from noisy single-photon data.Most current image reconstruction methods for single-photon data are mathematical approaches,which limits information utilization and algorithm performance.In this work,we propose a hybrid information enhancement model which can significantly enhance the efficiency of information utilization by leveraging attention mechanisms from both spatial and channel branches.Furthermore,we introduce a structural feature enhance module for the FFN of the transformer,which explicitly improves the model's ability to extract and enhance high-frequency structural information through two symmetric convolution branches.Additionally,we propose a single-photon data simulation pipeline based on RAW images to address the challenge of the lack of single-photon datasets.Experimental results show that the proposed method outperforms state-of-the-art methods in various noise levels and exhibits a more efficient capability for recovering high-frequency structures and extracting information.
关 键 词:single-photon images hybrid information enhancement structual feature enhancement data simulation pipeline
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