基于图像质量与频谱特性加权的光瞳结构优化

为进一步优化光学合成孔径成像系统的光瞳结构,研究了3种典型光瞳结构下复原图像质量与调制传递函数之间的关系,提出基于图像质量与频谱特性加权的光瞳优化方法。该方法将复原图像质量评价因子与频谱分布特性因子的线性加权值,作为光瞳优化的目标函数,调整权重因子使合成孔径成像系统的性能在图像质量与分辨率之间达到平衡。仿真结果表明,对六孔径和九孔径光瞳结构优化后,复原图像对比度更高,人工痕迹更少,更利于在保证成像分辨率的同时获得更好的成像质量。     

基于梯度边缘最大值的图像清晰度评价

光电遥感相机获取的遥感图像传回地面后都会出现一定降质模糊现象,通过复原可 以得到较高质量的遥感图像。得到复原图像后,需要一些方法来评价复原图像质量提高的程度。 为此提出了一种遥感图像清晰度的评价方法,通过提取图像的梯度,找出梯度最大的区域,计算 出该区域的像元个数,并以此作为图像的清晰度参数,计算复原图像清晰度与降质图像清晰度之 间的相对误差,得到清晰度提升率。通过设计实验来验证该方法的有效性。结果表明,该方法可 以准确评估复原图像的清晰度提升率,对弱振铃波纹有较好地抑制效果,在信噪比大于22 dB 时, 评价结果不受噪声影响。     

倒易晶胞和小波相结合的图像复原方法

为了去除混迭、噪声和模糊对图像的影响,从遥感器成像的光学及几何特性出发,运用倒易晶胞的复原方法对晶胞形状进行限制;将错误的频谱覆盖放到正确的位置以减少混迭;并结合小波复原方法对退化的图像进行去模糊、去噪,得到一幅更清晰的图像．实验结果表明,结合倒易晶胞的小波复原方法比常用基于频域的方法复原效果更好．     

散景效果的真实感绘制

针对现有方法绘制的散景效果真实感较差的问题,提出一种基于几何光学理论的散景效果真实感绘制方法.该方法以光线传播的折射定律为基础,利用序列光线追踪方法对相机镜头的光学成像特性进行精确建模;对相机镜头的内部结构进行精确模拟,包括孔径光阑和渐晕光阑,以绘制出由孔径形状和渐晕共同作用的散景效果;利用几何光学理论和序列光线追踪方法精确计算出出射光瞳的位置和大小,以辅助光线采样,提高光线追踪效率.绘制结果表明,利用该方法能够绘制出较为逼真的散景效果,正确模拟了孔径形状和渐晕对散景效果的影响,并具有较高的光线追踪效率.     

基于改进机器学习的超分辨率图像细节复原

相对于低分辨率图像,高分辨率图像需要增加的像素数目更多,且需要增加高频信息以提升图像的清晰度,当图像目标与背景之间对比度较大时,图像高频细节信息复原难度较高。为此,提出基于改进机器学习的超分辨率图像细节复原方法。对图像去噪,并结合采用双边滤波方法实现图像的对比度增强;利用改进字典的机器学习算法建立双层字典,结合稀疏表示算法获取一层的粗略复原图像;通过二层字典计算一层复原图像与原始图像之间的差值,建立高分辨率样本,并对其开展二层字典训练,通过训练结构实现超分辨率图像的细节复原。实验结果表明,研究方法应用下峰值信噪比可保持在20dB以上,细节复原均方差低于4×10^-3,结构相似性指标更高,高分辨率图像的训练效果更好,特征对比明显,细节信息突出。     

基于二次二维经验模态分解去噪的湍流退化图像复原算法

为实现大气湍流环境下的高质量成像,将自适应光学波前探测技术与数字图像处理技术相结合,并提出了一种基于二次二维经验模态分解去噪的湍流退化图像复原算法。通过在光学系统中使用哈特曼—夏克波前传感器探测波前信息,进而计算光学系统点扩散函数;然后使用改进的二次二维经验模态分解算法进行图像去噪,最后利用R-L算法实现对湍流退化图像的复原。通过搭建光学实验系统,对实际拍摄的湍流退化图像进行了复原实验。结果表明,该算法能够有效减弱噪声放大现象,得到更加稳定的高质量大气湍流退化图像复原结果。     

一种盲复原图像振铃效应的后处理与质量评价方法

由于图像盲复原过程中先验信息的不足,往往造成复原图像质量不尽如人意,振铃效应就是影响复原图像质量的重要因素之一。振铃效应的存在使得对复原图像的后续处理难于进行,并使得一些图像质量的评价方法失效。针对图像盲复原的特点,提出了一种振铃效应的后处理方法,并基于将振铃与清晰度分别评价的思想,提出了一种复原图像的无参考评价方案。实验证明该去振铃方法简单有效,该复原图像的评价方案也很好地反映了人类的视觉特性。     

月球撞击坑边缘清晰度评价方法的研究

撞击坑是月球表面最重要的地质构造之一,通过对“嫦娥一号”CCD影像中撞击坑的边缘清晰度进行评价,可以进一步反演出月球表面的风化程度、地表起伏等地质信息。提出一种基于图像清晰度评价的边缘清晰度评价方法,从空域的梯度、频域的高频分量以及信息论三个方面,运用基于Sobel算子、小波变换和信息熵的算法对撞击坑的边缘清晰度予以评价。设计出一种适应于月球撞击坑特征的BP神经网络,组合三种评价算法的结果作为其输入,进而得到最终的清晰度等级。将最终结果加载到具有自主知识产权的数字月球平台上予以全月性的展示和进一步分析。     

光学成像系统空间移变降质的最小二乘约束复原

对光学成像系统引起图像的空间移变降质，可以根据光学系统的特性函数确定相应的或者近似的逆（复原）函数，以数字图像处理的方法进行恢复，这些复原函数仍然具有空间移变的性质。本文在多项式近似逆滤波图像恢复方法的基础上，提出将空间移变的复原函数分解成空间移不变的基函数及其各次幂的线性组合，给出的基函数可以消除在图像恢复中用数字图像的差分代替图像的导数引入的误差，并在复原函数中引入最小二乘约束，对恢复图像进行规整，得到空间移变降质图像的最小二乘约束复原。获得的复原图像，是降质图像偶数阶差分的线性组合，组合系数由成像系统的点扩散函数、约束算子、复原规整参数和分解基函数确定。对模拟和实际获取的空间移变降质图像的处理结果表明，这种方法对各种信噪比条件下的图像恢复均适用。     

彩色叶片图像去尘算法

针对户外降尘造成叶片图像中颜色失真、细节信息模糊的问题,提出一种基于光学模型的单幅图像的自动去尘算法.首先根据光线在尘土层和空气中的传播方式建立了一种退化模型;然后结合光学反射成像模型和暗元色原理估计出环境光强及传输量2个模型参数;最后在该退化模型的基础上实现叶片图像尘土的快速去除,并用有尘土层的标准色卡和葡萄叶片图像检验算法的有效性.实验结果表明,文中算法对2种图像均有较好的去尘效果,复原后图像的H和S分量与无尘图像的偏离程度得到了明显改善,重现了图像的颜色和清晰度,获得了满意的视觉效果;该算法对不同天气和照明条件、不同品种的葡萄叶片图像均有较好的颜色恢复效果.     

一种适用于宽带条带式合成孔径声纳的相位梯度自聚焦算法

传统的相位梯度自聚焦(PGA,phase gradient autofocus)算法仅适用于窄带聚束式合成孔径成像。提出了一种适用于宽带条带式合成孔径声纳的相位梯度自聚焦(SPGA,stripmap phase gradient autofocus)算法。SPGA算法通过多普勒频移估计线性侧摆;SPGA算法消除了侧摆对回波信号包络的影响,使成像模糊仅仅是由方位向相位误差引起的,这样才可以由Dechirp后的距离压缩域数据的相位误差梯度得到侧摆的估计;SPGA算法利用估计得到的侧摆对距离徙动校正前的回波数据进行包络和相位补偿。仿真表明该算法有效地估计出了侧摆,提高了成像质量,且成像没有发生方位向偏移,明确地确定出了目标的位置。     

一种恢复综合孔径微波辐射计展源图像的CLEAN算法

CLEAN算法在处理对象是点源时,即使系统噪声很大,恢复点源图像的效果仍然比较理想,且思想简单、易于实现,因此在综合孔径微波辐射计点源图像的恢复中得到了广泛的应用和发展.但当处理对象是展源时,该算法恢复的辐射计图像存在着条纹现象.借鉴最大熵图像复原算法的约束思想,提出了一种改进的CLEAN算法.仿真结果表明,图像质量得到了进一步改善.     

A novel Scan SAR imaging method for maritime surveillance via Lp regularization

In this article, a novel Scan mode synthetic aperture radar (SAR) imaging method for maritime surveillance is presented. Conventional Scan SAR is generally operated with severe azimuth resolution loss in order to cover a large area. The proposed imaging method changes the way Scan SAR illuminates sub-scenes and presents a new radar illuminating strategy based on ships’ spatial distribution in each sub-scene. To gain ships’ spatial distribution, a scene sensing algorithm based on radar range profiles together with a peak-seeking and clustering algorithm is introduced. After that, a Markov transfer-probability matrix is generated to make sure that radar illuminates each sub-scene randomly under the probability we calculated before. Finally, an imaging algorithm within the L_p (0 < p ≤ 1) regularization framework is utilized to reconstruct each sub-scene; the regularization problem is solved by an improved iterative thresholding algorithm. The whole wide swath image is joined by putting all the sub-scenes together. Experimental results support that the proposed imaging method can perform high-resolution wide swath SAR imaging effectively and efficiently without reducing the image resolution.     

Sparse synthetic aperture radar imaging with optimized azimuthal aperture

To counter the problem of acquiring and processing huge amounts of data for synthetic aperture radar(SAR)using traditional sampling techniques,a method for sparse SAR imaging with an optimized azimuthal aperture is presented.The equivalence of an azimuthal match filter and synthetic array beamforming is shown so that optimization of the azimuthal sparse aperture can be converted to optimization of synthetic array beamforming.The azimuthal sparse aperture,which is composed of a middle aperture and symmetrical bilateral apertures,can be obtained by optimization algorithms(density weighting and simulated annealing algorithms,respectively).Furthermore,sparse imaging of spectrum analysis SAR based on the optimized sparse aperture is achieved by padding zeros at null samplings and using a non-uniform Taylor window.Compared with traditional sampling,this method has the advantages of reducing the amount of sampling and alleviating the computational burden with acceptable image quality.Unlike periodic sparse sampling,the proposed method exhibits no image ghosts.The results obtained from airborne measurements demonstrate the effectiveness and superiority of the proposed method.     

混沌信号雷达SAR成像的新方法

针对现有的合成孔径雷达(SAR)成像方法,介绍了隐抽样平均算法的基本原理及时延确定的方法;进一步,将隐抽样平均算法用于SAR成像中,提出了基于隐抽样平均的SAR成像算法;该成像算法利用二元混沌信号作为雷达发射信号,通过产生一个特征波形来指示目标的时延;与传统的SAR相关成像算法相比,该成像算法不仅简单易行,运算量小,成像时间短,成像效率高,而且重构图像具有较高的图像分辨率;仿真结果表明,图像中的目标轮廓清晰,图像品质较高。     

3-D position sensing using a passive monocular vision system

Passive monocular 3-D position sensing is made possible by a new calibration scheme that relates depth to focus blur through a composite lens and aperture model. The calibration technique enables the recovery of absolute 3-D position coordinates from image coordinates and measured focus blur. A geometric model of the camera's position and orientation in space is used to transform the camera's imaging coordinates into world coordinates. The relationship between the world coordinate system and the screen coordinate system which includes the amount of focus blur, is developed by modeling the camera imaging arrangement. The modeling proceeds first through the perspective view from a pinhole camera located anywhere in space. The camera's lens and aperture system is investigated to find the relationship between depth and focus blur. The aspect ratio of the frame image is considered. Position accuracies comparable to those in stereo based vision systems are possible without the need for solving the difficult point of correspondence problem     

Half-sweep imaging for depth from defocus

Depth from defocus (DFD) is a technique that restores scene depth based on the amount of defocus blur in the images. DFD usually captures two differently focused images, one near-focused and the other far-focused, and calculates the size of the defocus blur in these images. However, DFD using a regular circular aperture is not sensitive to depth, since the point spread function (PSF) is symmetric and only the radius changes with the depth. In recent years, the coded aperture technique, which uses a special pattern for the aperture to engineer the PSF, has been used to improve the accuracy of DFD estimation. The technique is often used to restore an all-in-focus image and estimate depth in DFD applications. Use of a coded aperture has a disadvantage in terms of image deblurring, since deblurring requires a higher signal-to-noise ratio (SNR) of the captured images. The aperture attenuates incoming light in controlling the PSF and, as a result, decreases the input image SNR. In this paper, we propose a new computational imaging approach for DFD estimation using focus changes during image integration to engineer the PSF. We capture input images with a higher SNR since we can control the PSF with a wide aperture setting unlike with a coded aperture. We confirm the effectiveness of the method through experimental comparisons with conventional DFD and the coded aperture approach.     

高炉燃烧料面合成孔径雷达双聚焦成像算法

钢铁生产中的料位和料形是冶炼过程控制的重要指标,基于合成孔径雷达(SAR)原理的微波成像装置可有效提取热态径向料面特征.针对几何形态复杂,回波信号突变性强,甚至会出现遮蔽效应导致雷达失波的料面中心部分,本文建立了中心区域双聚焦测量成像系统.选定炉顶风罩位置对称安装两套雷达,电机控制雷达天线匀速摆动获取二维坐标矩阵,用重采样插值算法将其转换成像素点均匀的矩形灰度图像;根据尺寸参数确定炉心扫描重叠区间,并构建料面中心双聚焦融合算法(DfSAR)拼接双图像.与均值融合相比,图像互信息提高10.43%,清晰度改善23.91%.最后,采用雷达恒虚警率(CFAR)目标检测算法有效滤除高炉内粉尘颗粒物对料面图像的干扰.     

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization基于伪极坐标变换和原子范数最小化的网格偏离目标DLSLA 3-D SAR成像方法

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.