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 sev...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.展开更多
Hadamard single-pixel imaging is an appealing imaging technique due to its features of low hardware complexity and industrial cost.To improve imaging efficiency,many studies have focused on sorting Hadamard patterns t...Hadamard single-pixel imaging is an appealing imaging technique due to its features of low hardware complexity and industrial cost.To improve imaging efficiency,many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples.In this study,we propose an efficient Hadamard basis sampling strategy that employs an exponential probability function to sample Hadamard patterns in a direction with high energy concentration of the Hadamard spectrum.We used the compressed-sensing algorithm for image reconstruction.The simulation and experimental results show that this sampling strategy can reconstruct object reliably and preserves the edge and details of images.展开更多
文摘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.
基金supported by the Beijing Institute of Technology Research Fund Program for Young Scholars(No.202122012).
文摘Hadamard single-pixel imaging is an appealing imaging technique due to its features of low hardware complexity and industrial cost.To improve imaging efficiency,many studies have focused on sorting Hadamard patterns to obtain reliable reconstructed images with very few samples.In this study,we propose an efficient Hadamard basis sampling strategy that employs an exponential probability function to sample Hadamard patterns in a direction with high energy concentration of the Hadamard spectrum.We used the compressed-sensing algorithm for image reconstruction.The simulation and experimental results show that this sampling strategy can reconstruct object reliably and preserves the edge and details of images.
