使用现有边缘检测方法提取古代壁画的线稿，存在噪声干扰大且丢失信息较多的问题。本文提出一种融合像素差卷积的壁画最优波段线稿提取方法，利用最小噪声分离方法将壁画多光谱数据的有效信息和噪声分离，选择最优主成分波段进行线稿的提取。针对传统卷积提取图像梯度信息的问题，引入像素差卷积提高边缘检测的图像梯度信息。在侧输出网络加入尺度增强模块（SEM）丰富多尺度特征，同时针对像素级别不平衡引起的像素错误分类问题，设计了基于图像相似度的Dice损失函数策略，逐级最小化像素距离获得清晰图像边缘，并利用壁画数据集先验知识微调模型解决数据集不足的问题。实验结果表明，本文方法可以在壁画褪色和噪声较多的场景下提取出较为清晰的线稿，线稿图像的SSIM和RMSE均优于其他算法，分别提高了2%~10%和2%~4%；在公开数据集BIPED上对模型进行验证，所提方法的ODS和OIS较PiDiNet分别提高0.005和0.007。该方法对褪色及具有病害的壁画可以提取出清晰完整的线稿图像。

逐像元自适应增益成像系统通过在每个像元的电子链路中集成四档不同容积的积分电容，可以在确保高信噪比的前提下实现大动态范围的遥感成像需求。此成像系统在发射前测试时，由于甚低增益（ULG）的动态范围过大而实验室积分球能量有限，只能通过与低增益（LG）的比例系数递推来间接对ULG后半量程的输出特性标定；星载太阳定标器反射能量过大会导致高增益（HG）和中增益（MG）输出饱和也无法直接测定辐射定标系数，只可通过比例系数推定。提出一种星上增益比例系数测定的方案，分别利用四档增益的输出作为特征对实验图像分类，将不同成像目标的输出码值作为多个定标能级，利用最小二乘法线性拟合相邻增益输出后得到相邻增益的比例系数。此方案验证了实验室增益比例系数测定结果，同时在外场成像实验中用该方法计算得到的比例系数用于相邻两档增益中较低增益图像反演较高增益图像，结果与实际较高增益图像对比归一化均方误差大部分小于0.01、两图像结构相关系数基本在90%左右、数据相关系数达到90%。证明该方法测定的相邻两增益比例相关系数有较高准确性，在星上辐射定标时用于高增益辐射定标系数的递推求取有极大的可行性，解决了星上不能直接对HG、MG辐射定标的问题。

The array spatial light field is an effective means for improving imaging speed in single-pixel imaging. However, distinguishing the intensity values of each sub-light field in the array spatial light field requires the help of the array detector or the time-consuming deep-learning algorithm. Aiming at this problem, we propose measurable speckle gradation Hadamard single-pixel imaging (MSG-HSI), which makes most of the refresh mechanism of the device generate the Hadamard speckle patterns and the high sampling rate of the bucket detector and is capable of measuring the light intensity fluctuation of the array spatial light field only by a simple bucket detector. The numerical and experimental results indicate that data acquisition in MSG-HSI is 4 times faster than in traditional Hadamard single-pixel imaging. Moreover, imaging quality in MSG-HSI can be further improved by image stitching technology. Our approach may open a new perspective for single-pixel imaging to improve imaging speed.

Recently, there has been increased attention toward 3D imaging using single-pixel single-photon detection (also known as temporal data) due to its potential advantages in terms of cost and power efficiency. However, to eliminate the symmetry blur in the reconstructed images, a fixed background is required. This paper proposes a fusion-data-based 3D imaging method that utilizes a single-pixel single-photon detector and millimeter-wave radar to capture temporal histograms of a scene from multiple perspectives. Subsequently, the 3D information can be reconstructed from the one-dimensional fusion temporal data by using an artificial neural network. Both the simulation and experimental results demonstrate that our fusion method effectively eliminates symmetry blur and improves the quality of the reconstructed images.

低采样率下的高质量鬼成像（GI）对于科学研究和实际应用具有重要意义，为了在低采样率条件下重建高质量图像，提出了一种高质量的被动式压缩鬼成像重构算法（PCGI-LRC）。基于图像的非局域相似块堆叠而成的矩阵具有低秩和稀疏奇异值的假设，从理论和实验上证明了一种对最小二乘问题与非局域相似块低秩近似问题进行联合迭代求解的方法，能够在低采样率（6.25%~50%）条件下实现高质量鬼成像。实验结果表明：与基于稀疏基约束的GI（GI-SBC）和基于全变分约束的GI（GI-TVC）相比，PCGI-LRC在峰值信噪比、结构相似性系数和视觉观测等方面均更优，在抑制重构噪声的同时保持了目标的细节信息，其中PSNR提升效果优于1.1 dB，SSIM提升效果优于0.04。