高准确度光学系统中材料非均匀性对成像质量的影响

利用Zernike多项式对用Zygo干涉仪测得的离散材料折射率数据进行了拟合,再使用光线光学的方法评价了系统的成像质量.由于材料折射率分布的无规则性,在对包含非均匀介质的实际光学系统的模拟仿真和优化时,需要考虑选取材料不同部位加工成的透镜会对系统成像质量有不同的影响,而且加工好的透镜在装配过程中,绕着光轴旋转不同的角度同样会影响成像质量.通过计算机模拟的方法预先选取材料的最佳部位以及找到最好的装配位置,从而提高了光学系统的性能.     

热致梯度折射率对非球面光学系统成像质量的影响

机载相机在上升过程中,由于温度降低和镜头各部分材料的导热率大小不同,镜头部分会产生温度梯度,从而导致非球面光学系统产生梯度折射率.利用有限元分析软件对镜头部分进行瞬态热分析以模拟机载相机上升过程中温度的变化情况,将有限元分析不同时间节点温度分布结果导入编写的折射率梯度系数拟合程序,将所求解的梯度折射率系数通过镜面类型接口在光学软件中建立光学系统模型,利用点列图的弥散斑半径评价不同时间节点梯度折射率对成像质量的影响.结果表明,机载相机刚升入工作高度时温度梯度最大,对成像质量影响也最严重,梯度折射率系数随着温度梯度系数的降低而降低,同时成像质量提高,该结果对光学系统的设计具有指导意义.     

六角形孔径平面微透镜阵列的制作及基本特性研究

采用光刻离子交换工艺制作了六角形孔径平面微透镜阵列.离子交换后的变折射率区域有微小重叠,填充系数可达95%以上.对制作的六角形孔径平面微透镜进行了折射率分布、数值孔径和像差等光学特性测试,得到的六角形孔径平面微透镜阵列光学性能均匀,成像质量较好的结果.该透镜阵列的研制成功为制作高质量高填充系数透镜阵列及模拟生物复眼光学系统奠定了基础.     

自聚焦透镜的三维相干成像特性分析

运用傅里叶光学方法，分析并推导了自聚焦透镜的三维相干成像公式。研究表明，虽然自聚焦透镜中介质折射率是径向渐变折射率分布，不同于薄透镜中的均匀分布，但是由于它的三维相干成像性质却类似于薄透镜，从而使自聚焦透镜可以在成像和光学信息处理系统中有效地代替薄透镜，实现光学系统的微型化。     

折射率、色散变化量与宽谱段傅氏镜二级光谱变化量的分析

介绍了宽谱段傅里叶变换镜头中光学玻璃的折射率、色散变化对系统的成像质量的影响.推导了折射率、色散变化量所引起的光学系统二级光谱的变化量公式.重点讨论了在宽谱段光学系统中，光学玻璃在折射率、色散上的变化量，所造成的胶合薄透镜的二级光谱的变化量.其系数在本文例中达0.28，相当于变化量占理论二级光谱余量的28％，因此在宽谱段系统中的二级光谱余量的变化量不应该被忽略.实例表明光学玻璃的折射率、色散变化量对宽谱段傅里叶变换镜头的成像质量有显著的影响.此外，还考虑了傅里叶变换透镜的波像差问题，其设计值小于1/10波长，采用最优玻璃对组合，可以保证波像差小于1/10波长，完全满足使用要求.     

中心偏误差对非球面透射式光学系统像差的影响

透射式光学系统中透镜中心偏误差的存在,影响了光学系统的共轴性,从而产生了偏心像差,影响了成像质量.非球面透镜的应用可以减少光学系统的像差,越来越被广泛使用.为了研究非球面透镜和球面透镜的中心偏误差带来的系统偏心像差的差异,提出了一种最佳光轴拟合对光学系统中心偏误差分析的方法.结合塞德尔多项式,利用Zemax软件对系统加...     

微面形貌观测物镜的光学系统误差分析

提出了一种非接触内窥式微面形貌探测镜,分析了其光学系统的装配误差对实际成像位置的影响.利用几何光学原理,计算了三种装配误差与实际成像位移之间的关系.给出了满足CCD成像分辨率要求的位置调整范围,推导出透镜的装配误差范围,达到成像清晰、系统分辨率高的目的.     

AGRIN混合光学系统设计

轴向梯度折射率(AGRIN)透镜是一种有望用来替代非球面,并在成像时优于非球面的一种方法.综述了AGRIN材料的发展历史,现状及其在镜头设计中的应用,简述AGRIN材料在光学设计中的应用和它与非球面相比的优点,探讨AGRIN材料的制造问题.发现使用AGRIN材料,不仅会使光学系统成像质量有所提高,而且在制造和测试上都有很大的优势.最后以设计一个焦距125 mm、F/4、视场25°,适用于DALSA全帧28M像素大幅面CCD的航测数字相机镜头为例,把原来的11片全球面镜头,利用2片AGRIN透镜,在保持像质不变的情况下减少到8片,而且像质均匀性和镜头结构更加合理.     

球面端面的锥形梯度折射率透镜的近轴成像特性

本文根据光线在球面上的折射公式及光线微分方程,研究了光线经两端面为球面的锥形梯度折射率透镜的传播和变换,基于光线传递ABCD矩阵,提出了球面端面的锥形梯度折射率透镜的一种等效光学系统.文中给出了该透镜的主平面、焦平面和焦距计算公式,以及近轴成像高斯公式.当锥度为零时即得到球面端面的柱形或径向梯度折射率透镜的相应结果.     

变折射率方形孔径平面微透镜阵列的聚焦和散焦特性

为提高变折射率平面微透镜阵列的填充率,利用光刻工艺和离子交换技术制备了填充率近达100%的方形孔径平面微透镜阵列,并对其透镜元及相邻透元间间隙构成的角落区域的成像进行了理论和实验研究.根据变折射率介质光线追迹法,利用MATLAB软件模拟,发现在透镜元区域和角落区域成像特性相反.成像系统测试表明:由于透镜元区域和角落区域的折射率分布变化规律不同,透镜元与角落区域对物体分别成倒立实像和正立虚像;透镜阵列可实现聚焦和散焦功能;角落区域得到充分的离子交换使得间隙足够小,形成了从该区域中心向外逐渐增大的新型梯度折射率模型.     

基于人眼视觉系统的假彩色融合图像质量的评价方法

随着图像融合技术的发展,各种融合算法层出不穷,而很多情况下最终的融合图像是由人眼观察的,因此基于人眼视觉系统的图像融合质量评价显得尤为重要.为了能够模拟人眼对于融合图像的感知,得到融合后图像质量的客观评价,本文提出了一种基于色差理论的假彩色融合图像质量的评价方法.首先将源图像和融合图像转化到CIE L*a*b*均匀色空间,在频域对图像进行对比度敏感函数滤波,通过计算滤波后融合图像的色差判断图像的细节信息,在一定程度上色差越大信息越丰富;通过计算融合图像与源图像的色差判断融合图像与源图像的相关性,相关性越高,融合算法越好.通过融合图像的色差大小以及与源图像的相关性两个参量,得出融合算法的优劣.实验表明,与其他评价方法相比,本文提出的评价方法与人眼观察的结果较为一致.     

潜在低秩表示框架下基于卷积神经网络结合引导滤波的红外与可见光图像融合

为提高融合图像的可视性,解决传统红外与可见光图像融合算法中存在的边缘特征缺失、细节模糊的问题,提出了一种潜在低秩表示框架下基于卷积神经网络结合引导滤波的红外与可见光图像融合算法。该算法首先利用潜在低秩表示对源图像进行分解,得到源图像的低秩分量和显著分量。其次,利用卷积神经网络根据源图像的特征信息,得到权值图。再次,通过引导滤波算法对权值图进行边缘锐化,然后再将优化后的权值图分别与源图像的低秩分量和显著分量融合,得到融合图像的低秩分量和显著分量。最后,将融合图像的低秩分量和显著分量叠加,得到最终的融合图像。实验结果表明,该算法在主观评价和客观指标上均优于传统的红外与可见光图像融合算法。     

基于DTCWT和稀疏表示的红外偏振与光强图像融合

针对红外偏振与光强图像彼此包含共同信息和特有信息的特点,提出了一种基于双树复小波变换和稀疏表示的图像融合方法.首先,利用双树复小波变换获取源图像的高频和低频成分,并用绝对值最大值法获得融合的高频成分;然后,用低频成分组成联合矩阵,并使用K-奇异值分解法训练该矩阵的冗余字典,根据该字典求出各个低频成分的稀疏系数,通过稀疏系数中非零值的位置信息判断共有信息和特有信息,并分别使用相应的规则进行融合;最后,将融合的高低频系数经过双树复小波反变换得到融合图像.实验结果表明,本文提出的融合算法不仅能较好地凸显源图像的共有信息,而且能很好地保留它们的特有信息,同时,融合图像具有较高的对比度和细节信息.     

Multifocus image fusion based on features contrast of multiscale products in nonsubsampled contourlet transform domain

In this paper, an efficient multifocus image fusion approach is proposed based on local features contrast of multiscale products in nonsubsampled contourlet transform (NSCT) domain. In order to improve the robustness of the fusion algorithm to the noise and select the coefficients of the fused image properly, the multiscale products, which can distinguish edge structures from noise more effectively in NSCT domain, is developed and introduced into image fusion field. The selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail. To improve the quality of the fused image, novel different local features contrast measurements, which are proved to be more suitable for human vision system and can extract more useful detail information from source images and inject them into the fused image, are developed and used to select coefficients from the clear parts of subimages to compose coefficients of fused images. Experimental results demonstrate the proposed method performs very well in fusion both noisy and noise-free multifocus images, and outperform conventional methods in terms of both visual quality and objective evaluation criteria.     

Fusion method of infrared and visible images based on neighborhood characteristic and regionalization in NSCT domain

On fusing infrared and visible image, the traditional fusion method cannot get the better image quality. Based on neighborhood characteristic and regionalization in NSCT (Nonsubsampled Contourlet Transform) domain, the fusion algorithm was proposed. Firstly, NSCT was adopted to decompose infrared and visible images at different scales and directions for the low and high frequency coefficients, the low frequency coefficients which were fused with improving regional weighted fusion method based on neighborhood energy, and the high-frequency coefficients were fused with multi-judgment rule based on neighborhood characteristic regional process. Finally, the coefficients were reconstructed to obtain the fused image. The experimental results show that, compared with the other three related methods, the proposed method can get the biggest value of IE (information entropy), MI(VI,F) (mutual information from visible image), MI(VI,F) (mutual information from infrared image), MI (sum of mutual information), and Q^AB/F (edge retention). The proposed method can leave enough information in the original images and its details, and the fused images have better visual effects.     

Multimodality Image Fusion Based on Quantum Wavelet Transform and Sum-Modified-Laplacian Rule

The parallelism and entanglement characteristics of quantum computation greatly improve the efficiency of image processing tasks. With the sharp increase of data size and requirement of real-time processing in image fusion application, rapid implementation using quantum computation will become the inexorable trend. A novel multimodality image fusion algorithm based on quantum wavelet transform (QWT) and proposed quantum version of sum-modified-laplacian (SML) rule is designed in this paper. The source digital images are firstly represented by flexible representation of quantum image (FRQI) model, and then the quantum form images are transformed with QWT to capture salient features of source images. The quantum version of SML rule is proposed to fuse wavelet coefficients, which has higher efficiency and runs faster than its classical counterpart. The final fused image is obtained by using inverse quantum wavelet transform. The simulations and theoretical analysis verify that the proposed algorithm is effective in the fusion of multimodality images.

     

基于可见光的多波段偏振图像融合新算法

采用了一种新的基于小波变换的偏振图像融合算法.首先,将两个波段中的每一波段三幅偏振图像利用小波变换分解成低频和高频部分,低频的小波系数平均值作为融合后的低频系数,高频细节系数根据不同区域特征选择方法以及对应输入图像小波系数的窗口区域方差来确定融合后高频小波系数,得到一个波段一幅图像.接着,将得到的图像再进行小波分解,采用低频图像的小波系数最小值作为融合后的低频系数,高频图像根据纹理一致性测度的纹理检测确定融合规则,用来调整高频小波系数,将来自不同图像的特征与细节融合在一起,并对融合图像质量进行了对比评价.实验结果表明,融合后的偏振图像不仅反映了场景的偏振信息,而且还包含了丰富的光谱信息,目标与背景的衬比度也得到了增强,为进一步的目标检测和识别提供了便利.     

A false color image fusion method based on multi-resolution color transfer in normalization YCBCR space

In this paper, a false color image fusion method for merging visible and infrared images is proposed. Firstly, the source images and reference image are decomposed respectively by Laplacian pyramid transform. Then the grayscale fused image and the difference images between the normalized source images are assigned to construct YC_BC_R components. In the color transfer step, all the three channels of the color space in each decomposition level are processed with the statistic color mapping according to the color characteristics of the corresponding sub-images of the reference image. Color transfer is designed based on the multi-resolution scheme in order to significantly improve the detailed information of the final image, and to reduce excessive saturation phenomenon to have a comparatively natural color appearance compared with the classical global-coloring algorithm. Moreover, the differencing operation between the normalized source images not only provides inter-sensor contrast to make popping the potential targets but also weakens the influence of the ambient illumination variety to a certain degree. Finally, the fused results and several metrics for evaluation of fused images subjectively and objectively illustrate that the proposed color image fusion algorithm can yield a more complete mental representation of the perceived scene, resulting in better situational awareness.     

The infrared and visible image fusion algorithm based on target separation and sparse representation

Although the fused image of the infrared and visible image takes advantage of their complementary, the artifact of infrared targets and vague edges seriously interfere the fusion effect. To solve these problems, a fusion method based on infrared target extraction and sparse representation is proposed. Firstly, the infrared target is detected and separated from the background rely on the regional statistical properties. Secondly, DENCLUE (the kernel density estimation clustering method) is used to classify the source images into the target region and the background region, and the infrared target region is accurately located in the infrared image. Then the background regions of the source images are trained by Kernel Singular Value Decomposition (KSVD) dictionary to get their sparse representation, the details information is retained and the background noise is suppressed. Finally, fusion rules are built to select the fusion coefficients of two regions and coefficients are reconstructed to get the fused image. The fused image based on the proposed method not only contains a clear outline of the infrared target, but also has rich detail information.     

Visual attention guided image fusion with sparse representation

Image fusion techniques aim at transferring useful information from the input source images to the fused image. The common assumption for most fusion approaches is that the useful information is defined by local features such as contrast, variance, and gradient. However, there is no consideration of global visual attention of the whole source images which indicates the “interesting” information of the source images. In this paper, we firstly review the patch-based image fusion methods which attract the attention and interest of many researchers. Then, a visual attention guided patch-based image fusion method is proposed. The visual attention maps of the source images are calculated from the sparse represent coefficients of the source images. Then, the sparse coefficients are fused with the guidance of visual attention maps in order to emphasize the global “interesting” objects in the source images. Finally, the fused image is reconstructed from the fused sparse coefficients. The new fusion strategy ensures that the objects being “interesting” for our visual system are preserved in the fused image. The proposed approach is tested on infrared and visual, medical, and multi-focus images. The results compared with those of traditional methods show obvious improvement in objective and subjective quality measurements.