The prediction of fundus fluorescein angiography (FFA) images from fundus structural images is a cutting-edge research topic in ophthalmological image processing. Prediction comprises estimating FFA from fundus camera imaging, single-phase FFA from scanning laser ophthalmoscopy (SLO), and three-phase FFA also from SLO. Although many deep learning models are available, a single model can only perform one or two of these prediction tasks. To accomplish three prediction tasks using a unified method, we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network. The three prediction tasks are processed as follows: data preparation, network training under FFA supervision, and FFA image prediction from fundus structure images on a test set. By comparing the FFA images predicted by our model, pix2pix, and CycleGAN, we demonstrate the remarkable progress achieved by our proposal. The high performance of our model is validated in terms of the peak signal-to-noise ratio, structural similarity index, and mean squared error.

针对无法实现先验的边缘检测场景，并解决边缘提取效率过低的问题，提出一种更高效的基于傅里叶单像素成像的亚像素级边缘检测方法。该方法结合快速傅里叶单像素成像，减少图像算法的相移步数，在原有四步相移的基础上分别实现了三步相移与两步相移边缘检测。该算法上的改进能够在同等采样数下扩大参与边缘提取的频谱宽度，从而提升边缘提取效率。数值仿真结果表明，与四步相移亚像素级边缘检测相比，无噪声条件下两步相移在 655~13100次左右的采样数区间内峰值信噪比增长幅度高出 2.27dB，噪声条件下低于 0.054噪声浮动比率时两步相移方法可以获得比四步相移更高的边缘提取质量。该方法可以一定程度上提升边缘提取效率，同时促进单像素成像领域与图像处理方向的技术交叉和应用化发展。

Automatic cell counting provides an effective tool for medical research and diagnosis. Currently, cell counting can be completed by transmitted-light microscope, however, it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells. Further, the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope, automatically and effectively. In this work, a new deep-learning (DL)-based two-stage detection method (cGAN-YOLO) is designed to further enhance the performance of cell counting, which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model. The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images. Compared with the previously reported YOLO-based one-stage detection method, high recognition accuracy (RA) is achieved by the cGAN-YOLO method, with an improvement of 29.80%. Furthermore, we can also observe that cGAN-YOLO obtains an improvement of 12.11% in RA compared with the previously reported image-translation-based detection method. In a word, cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance, which extends the applicability in clinical research.

针对Intensity ScanContext（ISC）等激光雷达闭环检测算法中描述子易受载体旋转和平移变化干扰、不变性较弱从而导致闭环效果差的问题，提出了一种在城市动态环境下具有旋转和平移不变性的激光雷达点云描述子。首先，在点云处理部分，基于角度图像的地面点分割算法去除点云数据中的地面点，并利用基于弯曲体素聚类的动态目标剔除算法完成点云分割和动态目标的剔除。然后，基于惯性测量元件采集的四元数对点云进行坐标系变换，将所有帧点云统一至同一航向角下，以实现描述子的旋转不变性。接着，通过点云中静态目标与载体运动方向所在法平面的相对位置关系来确定描述子绘制中心，以实现描述子的平移不变性。最后，利用所提描述子进行闭环检测，并结合点云数据的空间结构对闭环检测结果进行一致性验证。在KITTI公开数据集下的实验结果表明，所提方法在城市动态环境下能够更加快速准确地实现闭环检测，相比于ISC算法，所提方法在准确率为100%时召回率提高了8.58个百分点，且平均耗时减少了12.90%。

本文针对共轭平移差分方法中的关键参数进行研究，提出了一种基于逆向优化策略的平移量最优解确定方法。首先对差分逼近精度及信噪比进行研究，建立噪声条件下的平移量与面形重建误差模型；然后将被测面面形估计值和随机噪声引起的面形偏差作为变量，利用差分Zernike算法求解面形重建容许误差下的最佳平移量区间；最后采用商用干涉仪对标准镜进行不同平移量下的面形绝对检测对比实验。实验结果表明：平移量的取值会直接影响面形测量精度，在最佳平移区间内进行面形绝对检测所得结果与传统三面互检方法所得结果基本吻合，证明了所提方法可以有效提高平面光学元件的面形测量精度。

圆形正弦条纹在条纹圆心处具有恒定相位（常编码为零相位），其圆心可以作为相位展开的参考点，据此得到的绝对相位用于计算被测物体的高度信息。由于圆形条纹的载波相位为非线性函数，已有的圆形条纹投影傅里叶变换法需要求解一元二次方程，进行判根操作，再使用拟合来得到对应物面高度信息的像素位移量，鲁棒性差。提出并研究了一种基于傅里叶变换的改进圆形条纹投影轮廓术，该技术通过多投影一帧具有水平移动量的条纹，简化了圆形条纹投影方式的像素位移量的计算，将像素位移量的计算从解一元二次方程降维为解一元一次方程，提高了基于傅里叶变换圆形条纹投影轮廓术的鲁棒性和面形重建精度。计算机仿真和实验验证了所提方法重建物体三维面形的有效性，特别适合全平面离面测量。