【摘要】 在量子图像传感器(QIS)搭建的单光子成像系统中，场景信息蕴含于QIS输出的二值量化数据中，从二值比特流重建原始图像为极度不适定问题。针对现有重建算法在低过采样率重建质量低，对读出噪声敏感的问题，该文提出一种基于扩散模型和边缘信息引导的QIS图像重建算法，以实现快速高质量重建。该算法将测量子空间约束引入无条件的扩散模型反向扩散过程以满足数据一致性和自然图像数据分布的要求，最大似然估计算法重建图像的边缘轮廓成分作为辅助信息引导采样，在减少采样步数的同时提升重建质量。该算法在多个通用数据集上进行测试，并与典型的QIS图像重建算法和基于扩散模型的方法进行比较，实验结果表明，该算法有效地改善了图像重建质量，且对读出噪声具有较强的鲁棒性。更多还原

【Abstract】 Objective Quanta Image Sensors(QIS) are solid-state sensors that encode scene information into binary bitstreams. The reconstruction for QIS consists of recovering the original scenes from these bit-streams, which is an ill-posed problem characterized by incomplete measurements. Existing reconstruction algorithms based on physical sensors primarily use maximum-likelihood estimation, which may introduce noise-like components and result in insufficient sharpness, especially under low oversampling factors. Model-based optimization algorithms for QIS generally combine the likelihood function with an explicit or implicit image prior in a cost function.Although these methods provide superior quality, they are computationally intensive due to the use of iterative solvers. Additionally, intrinsic readout noise in QIS circuits can degrade the binary response, complicating the imaging process. To address these challenges, an image reconstruction algorithm, Edge-Guided Diffusion Model(EGDM), is proposed for single-photon sensors. This algorithm utilizes a diffusion model guided by edge information to achieve high-speed, high-quality imaging for QIS while improving robustness to readout noise. Methods The proposed EGDM algorithm incorporates a measurement subspace constrained by binary measurements into the unconditional diffusion model sampling framework. This constraint ensures that the generated images satisfy both data consistency and the natural image distribution. Due to high noise intensity in latent variables during the initial reverse diffusion stages of diffusion models, texture details may be lost, and structural components may become blurred. To enhance reconstruction quality while minimizing the number of sampling steps, a bilateral filter is applied to extract edge information from images generated by maximum likelihood estimation. Additionally, the integration of jump sampling with a measurement subspace projection termination strategy reduces inference time and computational complexity, while preserving visual quality.Results and Discussions Experimental results on both the benchmark datasets, Set10 and BSD68(Fig. 6, Fig.7, Table 2), and the real video frame(Fig. 8) demonstrate that the proposed EGDM method outperforms several state-of-the-art reconstruction algorithms for QIS and diffusion-based methods in both objective metrics and visual perceptual quality. Notably, EGDM achieves an improvement of approximately 0.70 dB to 3.00 dB compared to diffusion-based methods for QIS in terms of Peak Signal-to-Noise Ratio(PSNR) across all oversampling factors. For visualization, the proposed EGDM produces significantly finer textures and preserves image sharpness. In the case of real QIS video sequences(Fig. 8), EGDM preserves more detailed information while mitigating blur artifacts commonly found in low-light video capture. Furthermore, to verify the robustness of the reconstruction algorithm to readout noise, the reconstruction of the original scene from the measurements is conducted under various readout noise levels. The experimental results(Table 3, Fig. 9, Fig.10) demonstrate the effectiveness of the proposed EGDM method in suppressing readout noise, as it achieves the lowest average Mean Squared Error(MSE) and superior quality compared to other algorithms in terms of PSNR, particularly at higher noise levels. Visually, EGDM produces the best results, with sharp edges and clear texture patterns even under severe noise conditions. Compared to the EGDM algorithm without acceleration strategies, the implementation of jump sampling and measurement subspace projection termination strategies reduces the execution time by 5 seconds and 1.9 seconds, respectively(Table 4). Moreover, EGDM offers faster computation speeds than other methods, including deep learning-based reconstruction algorithms that rely on GPU-accelerated computing. After thorough evaluation, these experimental findings confirm that the highperformance reconstruction and rapid imaging speed make the proposed EGDM method an excellent choice for practical applications.Conclusions This paper proposes a single-photon image reconstruction algorithm, EGDM, based on a diffusion model and edge information guidance, overcoming the limitations of traditional algorithms that produce suboptimal solutions in the presence of low oversampling factors and readout noise. The measurement subspace defined by binary measurements is introduced as a constraint in the diffusion model sampling process, ensuring that the reconstructed images satisfy both data consistency and the characteristics of natural image distribution. The bilateral filter is applied to extract edge components from the MLE-generated image as auxiliary information. Furthermore, a hybrid sampling strategy combining jump sampling with measurement subspace projection termination is introduced, significantly reducing the number of sampling steps while improving reconstruction quality. Experimental results on both benchmark datasets and real video frames demonstrate that:(1) Compared with conventional image reconstruction algorithms for QIS, EGDM achieves excellent performance in both average PSNR and SSIM.(2) Under different oversampling factors, EGDM outperforms existing diffusion-based reconstruction methods by a large margin.(3) Compared with existing techniques, the EGDM algorithm requires less computational time while exhibiting strong robustness against readout noise, confirming its effectiveness in practical applications. Future research could focus on developing parameter-free reconstruction frameworks that preserve imaging quality and extending EGDM to address more complex environmental challenges, such as dynamic low-light or high dynamic range imaging for QIS.更多还原