Light field imaging technology can obtain three-dimensional (3D) information of a test surface in a single exposure. Traditional light field reconstruction algorithms not only take a long time to trace back to the original image, but also require the exact parameters of the light field system, such as the position and posture of a microlens array (MLA), which will cause errors in the reconstructed image if these parameters cannot be precisely obtained. This paper proposes a reconstruction algorithm for light field imaging based on the point spread function (PSF), which does not require prior knowledge of the system. The accurate PSF derivation process of a light field system is presented, and modeling and simulation were conducted to obtain the relationship between the spatial distribution characteristics and the PSF of the light field system. A morphology-based method is proposed to analyze the overlapping area of the subimages of light field images to identify the accurate spatial location of the MLA used in the system, which is thereafter used to accurately refocus light field imaging. A light field system is built to verify the algorithm’s effectiveness. Experimental results show that the measurement accuracy is increased over 41.0% compared with the traditional method by measuring a step standard. The accuracy of parameters is also improved through a microstructure measurement with a peak-to-valley value of 25.4% and root mean square value of 23.5% improvement. This further validates that the algorithm can effectively improve the refocusing efficiency and the accuracy of the light field imaging results with the superiority of refocusing light field imaging without prior knowledge of the system. The proposed method provides a new solution for fast and accurate 3D measurement based on a light field.

辐射定标是卫星遥感信息定量化的关键技术之一。高分辨率光学遥感卫星小目标法在轨定标将目标反射率的现场测量转换为实验室高精度测量, 以实际测量代替气溶胶散射特性假设, 通过简化辐射传输计算获取大气透过率与遥感器入瞳辐亮度, 根据遥感器系统PSF检测结果将小目标的辐射响应与背景辐射相分离, 降低对场区背景环境要求的同时提高了绝对辐射定标精度。试验结果分析表明, 高分辨率光学遥感卫星传感器小目标法在轨辐射定标不确定度优于3%, 与大面积辐射校正场或灰阶靶标法的定标结果差异3.65%, 小目标法有望在全谱段范围实现高分辨率光学遥感卫星传感器的全动态范围定标与几何检校。

自适应光学(AO)系统为大气湍流提供实时补偿,但AO图像的对比度通常很差, 这会导致图像质量下降。本文提出了一种基于点扩散函数(PSF)重构和改进的最大后验概率(MAP)估计的自适应光学图像恢复方法。首先, 结合观测条件和AO系统的特点, 建立基于波前相位信息的PSF重构模型; 其次, 基于本文提出的算法, 建立了AO图像的迭代求解公式, 实现多帧AO图像联合去卷积过程。为了验证本文算法的有效性, 对仿真的退化AO图像进行了一系列复原实验。实验结果表明,以“Man”图像为例, 与Wiener-IBD、RL-IBD及FS-MLJD算法相比, 该算法的峰值信噪比(PSNR)测度分别提高了683%, 4.47%和2.28%, 拉普拉斯梯度模(LS)值分别提高了22.2%, 17.9%和12.1%。本文的研究结果对实际的AO图像恢复具有一定的应用价值。

通过强光辐照光电成像系统实验研究, 得到了饱和像元数与入射光强、光学系统参数、像元饱和阈值等参数之间的变化规律, 并利用光学系统点扩展函数(PSF)进行了理论验证。结果表明, 饱和像元主要由衍射效应引起。推导了饱和像元数与入射光强、波长、光学系统参数、像元饱和阈值等参数之间的关系。

In this paper, we propose a point spread function (PSF) reconstruction method and joint maximum a posteriori (JMAP) estimation method for the adaptive optics image restoration. Using the JMAP method as the basic principle, we establish the joint log likelihood function of multi-frame adaptive optics (AO) images based on the image Gaussian noise models. To begin with, combining the observed conditions and AO system characteristics, a predicted PSF model for the wavefront phase effect is developed; then, we build up iterative solution formulas of the AO image based on our proposed algorithm, addressing the implementation process of multi-frame AO images joint deconvolution method. We conduct a series of experiments on simulated and real degraded AO images to evaluate our proposed algorithm. Compared with the Wiener iterative blind deconvolution (Wiener-IBD) algorithm and Richardson-Lucy IBD algorithm, our algorithm has better restoration effects including higher peak signal-to-noise ratio (PSNR) and Laplacian sum (LS) value than the others. The research results have a certain application values for actual AO image restoration.

利用图像点扩散函数(PSF)近似计算法建立线阵探测器工业CT金属材料X射线衰减测量模型, 并利用该测量模型对垂直于X射线平面边界灰度幅值进行数值计算, 获得当前PSF曲线。在此基础上, 建立基于近似PSF曲线的缺陷检测理论模型, 并对缺陷进行定量检测分析。采用高能6 MeV工业CT线阵探测器系统, 对含有人工缺陷的不锈钢试块进行缺陷检测试验。结果表明: 相比于传统半高宽缺陷测量方法, 本实验方法在缺陷定量检测精度上有较大提高, 从而为提升金属材料内部缺陷的定量检测水平提供新的方法和技术途径。

对单一图像进行运动模糊复原, 存在模糊点扩散函数(PSF)难以估计以及图像反卷积的病态性问题。利用多个PSF具有联合可逆性的特点, 针对运动目标观测, 提出采用参数相同的多个成像设备共同对同一视场进行拍摄, 来获取背景相同、曝光时间不同、目标模糊程度不同的观测图像; 然后利用同一设备获取的序列图像进行目标的模糊PSF估计; 并根据目标背景的运动模糊叠加特征, 分别从观测图像中提取出完整的模糊目标图像; 最后, 对这些具有不同PSF的同一目标图像进行空间域迭代复原算式的联立求解。实验表明: 该方法设计的目标获取装置对硬件条件要求较低, 获取的图像更便于采用多点扩散函数联合进行图像复原, 复原效果良好。

天文导航是通过探测恒星并利用其在天体坐标中的已知位置信息,反解出探测平台姿态信息。无论是在可见光波段,还是在近红外波段对恒星进行探测,由于飞行器的快速运动和持续曝光时间的存在,在拍摄恒星图像时,容易造成星图的运动模糊,给后续的星图匹配和导航定位精度造成影响。针对星敏感器拍摄到的星图,在运动参数未知的情况下,利用Roberts边缘检测算子和自相关的方法分别确定出点扩散函数的方向和尺度参数,然后采用有约束的最小二乘滤波来复原恒星图像,取得了较好的效果,为天文导航后续的恒星位置确定及星图匹配奠定了基础。