内窥镜图像畸变校正的新方法

本文提出了基于全局收敛线性搜索的内窥镜图像桶形畸变的校正方法。方法假定畸变为圆对称形式,并利用多项式映射将畸变图像从畸变图像空间映射到校正图像空间。根据内窥镜成像特性以及基于全局收敛的线性搜索法来确定畸变图像中心和多项式系数。采用非线性回归对校正图像进行补偿。试验结果证实,该方法校正效果好、响应速度快、满足需要定量分析的图像处理的要求。     

Accurate geometric correction of ATSR images

An accurate geometric correction algorithm of raw ATSR images is proposed in this paper. The proposed algorithm is divided into two modules: pre-correction and precision correction. In the pre-correction algorithm, the scanning geometry of the ATSR, ephemeris data and the Earth's shape and rotation were modeled mathematically. It corrected elliptically-distorted raw ATSR images and resulted in a mean collocation error of 1 pixel between the nadir and forward images. The proposed navigation model showed a better geolocation performance than ATSR image geometric correction algorithms currently in operation. The geometric errors in the pre-corrected images were reduced further by the precision correction algorithm. Coastlines were detected from the pre-corrected nadir and forward images and matched with map coastline data. The matched coastline pixels, GCPs, were combined with the pre-correction model to estimate the errors of the position, velocity and attitude of the satellite. An extended Kalman filter was used for the optimal estimation. The corrected images showed the collocation error of 0.4 pixel and the geolocation error of 0.7 pixel. The results were compared with those from a polynomial warping technique. It proved that the proposed precision correction algorithm gave better geometric correction performance than collocation algorithms based on a polynomial warping technique     

A new method for distortion correction of electronic endoscope images

A new method to correct the barrel distortion of an electronic endoscope image is presented. A correction model assuming circularly symmetric distortion is introduced with the following model parameters: the center of distortion and the coefficients of polynomials representing the distortion correction in the radial direction. If the imaging system is distortion-free, straight lines in the object space should be imaged as straight lines. Based on this criterion, a distorted image of a standard pattern consisting of a grid of several straight lines is recorded, and the model parameters are then estimated as a basis for straightening distorted lines. This method has the advantage of not needing a careful placement of the standard pattern for calibration. Correction results are presented for the grid pattern chart to verify a sufficient degree of correction. Examples of distortion correction of real intestinal images and physicians' comments are also presented.     

MEMS振镜扫描共聚焦图像畸变机理分析及校正

在皮肤反射式共聚焦显微成像过程中，针对MEMS振镜二维扫描引起的共聚焦图像畸变，开展了光束偏转理论分析，得出了投影面扫描图像的具体形状表征，理论畸变图像与真实畸变图像一致，明确了畸变机理，提出一种有效的畸变校正算法，实现对图像二维畸变的校正。首先记录原始光栅畸变图像，然后基于Hessian矩阵提取光栅中心线，拾取特征点并设置基准参考线，通过基于最小二乘法的7次多项式插值法标定二维方向像素畸变校正量，采用加权平均法填补间隙像素灰度值，最终实现图像畸变校正。利用网格畸变测试靶实验得出7次多项式插值后的校正决定系数最高、均方根误差值最低，整幅512行图像在7次多项式插值后最优行数占379行，比例为74%，通过残差分析，二维方向上残差最大为4个像素，最小为0个像素，平均为1.15个像素，校正结果较为精确。皮肤在体实时成像实验显示，图像畸变校正后组织结构特征更加真实准确，表明这种校正算法有效可行，有助于皮肤疾病的准确诊断。     

Nonmetric calibration of camera lens distortion: differential methods and robust estimation.

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive new distortion measures that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also provide fast, closed-form solutions to the distortion coefficients. We prove that including both the distortion center and the decentering coefficients in the nonlinear optimization step may lead to instability of the estimation algorithm. Our approach provides a way to get around this, and, at the same time, it reduces the search space of the calibration problem without sacrificing the accuracy and produces more stable and noise-robust results. In addition, while almost all existing nonmetric distortion calibration methods needs user involvement in one form or another, we present a robust approach to distortion calibration based on the least-median-of-squares estimator. Our approach is, thus, able to proceed in a fully automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered projections of three-dimensional linear segments. Experiments to evaluate the performance of this approach on synthetic and real data are reported.     

Videoendoscopic distortion correction and its application to virtual guidance of endoscopy

Modern video-based endoscopes offer physicians a wide-angle field of view (FOV) for minimally invasive procedures. Unfortunately, inherent barrel distortion prevents accurate perception of range. This makes measurement and distance judgment difficult and causes difficulties in emerging applications, such as virtual guidance of endoscopic procedures. Such distortion also arises in other wide FOV camera circumstances. This paper presents a distortion-correction technique that can automatically calculate correction parameters, without precise knowledge of horizontal and vertical orientation. The method is applicable to any camera-distortion correction situation. Based on a least-squares estimation, our proposed algorithm considers line fits in both FOV directions and gives a globally consistent set of expansion coefficients and an optimal image center. The method is insensitive to the initial orientation of the endoscope and provides more exhaustive FOV correction than previously proposed algorithms. The distortion-correction procedure is demonstrated for endoscopic video images of a calibration test pattern, a rubber bronchial training device, and real human circumstances. The distortion correction is also shown as a necessary component of an image-guided virtual-endoscopy system that matches endoscope images to corresponding rendered three-dimensional computed tomography views.     

基于背景光修正成像模型的水下图像复原

光在水下传播时由于受到水体吸收和散射作用的影响,导致水下图像质量严重退化。为了有效去除色偏和模糊,改善水下图像质量,该文提出一种基于背景光修正成像模型的水下图像复原方法。该方法基于对雾天图像的观察,提出了水下图像背景光偏移假设,并基于此建立背景光修正成像模型;随后使用单目深度估计网络获得场景深度的估计,并结合背景光修正的水下成像模型,利用非线性最小二乘拟合获得水下偏移分量的估计值从而实现水下图像去水;最后优化去水后的含雾图像的透射率,并结合修正后的背景光实现图像复原。实验结果表明,该文方法在恢复水下图像颜色和去除散射光方面效果良好。     

基于失真图像特性测量的扫描图像校正技术

提出一种扫描图像校正技术,解决书籍扫描时图像背景灰度不匀、几何畸变和局部模糊的问题。该方法不依赖扫描设备的物理参数,完全根据对失真图像的测量来计算校正因子。选取适当的采样窗1：3,基于直方图分析导出背景灰度因子,通过内插校正几何失真,最后进行空变高通滤波以减轻字迹模糊。通过建立背景灰度因子和缩小系数的关系实现对扫描设备的标定,从而在校正同一设备输出的其它图像时可根据背景灰度的变化而不必再检测页面边缘。与原始扫描图像相比,处理后的图像质量大为改善,且光学字符识别（OCR）处理的识别率达到82．6％。     

基于离散Tchebichef变换的多聚焦图像融合方法

基于变换技术的图像融合是多聚焦图像融合中常采用的方法,其过程是将图像转换到变换域按照一定的融合规则进行融合后再反变换回来,具有较强的抗噪能力,融合效果明显。该文提出一种基于离散Tchebichef正交多项式变换的多聚焦图像融合方法,首次将离散正交多项式变换应用到多聚焦图像融合领域。该方法成功地利用了图像的空间频率与其离散Tchebichef正交多项式变换系数之间的关系,通过离散正交多项式变换系数直接得到空间频率值,避免了将离散多项式变换系数变换到空域计算的过程。所提方法节省了融合时间,并提高了融合效果。     

基于二维Gabor小波的人脸识别算法

该文提出了一种基于二维Gabor小波的人脸识别算法。该算法先对人脸图像进行多分辨率的Gabor小波变换,然后在图像上放置一组网格结点,每个结点用该结点处的多尺度Gabor幅度特征描述,采用主元分析法对每个结点进行去相关、降维,最后形成特征结。把每个特征结作为观测向量,对隐马尔可夫模型进行训练,并把优化的模型参数用于人脸识别。实验结果表明,该方法识别率高,复杂度较低。     

烟花算法在SAR图像属性散射中心参数估计中的应用

针对合成孔径雷达（SAR）属性散射中心估计问题,提出基于烟花算法的方法。首先,在图像域对SAR图像中高能量区域进行分割解耦,获得单个独立散射中心在图像域的表现形式。在此基础上,以属性散射中心参数化模型为基础,构建优化问题,对分离出来的单个散射中心进行最优参数的搜索。在此阶段,引入烟花算法进行参数寻优。该算法具有强大的全局和局部搜索能力,在保证优化精度的条件下避免陷入局部最优,从而保证散射中心参数估计的可靠性。在原始图像中剔除求解后的单个散射中心,对残余图像进行高能量区域分割,序惯估计下一个散射中心的属性参数。最终,获取输入SAR图像上所有散射中心的参数集。实验中,首先基于MSTAR数据集中的SAR图像进行参数估计验证,通过参数估计结果与原始图像的对比以及基于估计参数集对原始图像进行重构,反映了提出算法的有效性。此外,实验还基于估计得到的属性参数进行SAR目标识别算法验证,通过与其他参数估计算法在相同条件下进行识别性能的对比,进一步体现了提出方法在属性散射中心参数估计上的性能优势。     

A noise-immune no-reference metric for estimating blurriness value of an image

Blur is a type of distortion that may happen in digital images. Blur estimation is an important issue in image processing applications such as image deblurring and depth estimation. Several blur metrics exist in the literature, but they are mostly sensitive to the presence of noise. In this paper, a simple yet accurate no-reference blur metric with low computational cost is proposed, which is robust against noise. The proposed blur metric is based on the observation that there is a considerable difference between the DCT of a sharp image and the one associated with its blurred version. The effect of noise is mainly reflected in the higher order DCT coefficients. Hence, the noise effect is mitigated in this paper via discarding the higher order DCT coefficients. The experiments, performed on four databases (including CSIQ, TID2008, IVC, and LIVE), indicate the capability of the proposed metric in measuring image blurriness. Comparative results with other existing approaches show the superiority of the proposed blur metric, especially at the presence of noise.     

基于NSST和高斯混合模型的医学图像融合算法

提出了一种基于非下采样剪切波变换（Non-subsampled shearlet transform, NSST）和高斯混合模型的医学彩色图像融合算法。首先将彩色图像转换到HSI颜色空间,提取其色度分量图像、饱和度分量图像和强度分量图像;然后对强度分量图像和MRI图像进行NSST变换,其中低频系数采用基于区域系数改进拉普拉斯能量和（Sum-modified-Laplacian,SML）加权的融合规则,高频系数采用高斯混合模型估计参数取大的融合规则;对融合后的系数进行NSST逆变换重构融合后的强度分量图像;最后将融合后的强度分量图像与色度分量图像、饱和度分量图像混合得到融合后的HSI图像,再将HSI图像转换到RGB颜色空间得到融合后的彩色图像。仿真实验表明,该算法在视觉效果和客观评价指标上具有更好的融合效果。     

Transfer Function Computation for Multidimensional Systems

A theoretically interesting technique is proposed for the determination of the coefficients of the determinantal polynomial and the coefficients of the adjoint polynomial matrix of a given n-D system, described by the Fornasini-Marchesini state space model. The proposed algorithms are based on the discrete Fourier transform (DFT), and easily can be implemented. An example is included to illustrate the application of the algorithm.     

Optical flow estimation in cardiac CT images using the steered Hermite transform

This paper describes a new method to estimate the heart's motion in computer tomography images with the inclusion of a bio-inspired image representation model. Our proposal is based on the polynomial decomposition of each of the images using the steered Hermite transform as a representation of the local characteristics of images from an perceptual approach within a multiresolution scheme.The Hermite transform is a model that incorporates some of the more important properties of the first stages of the human visual system, such as the overlapping Gaussian receptive fields, the Gaussian derivative model of early vision and the multiresolution analysis.We propose an approach for optical flow estimation that incorporates image structure information extracted from the steered Hermite coefficients, that is later used as local motion constraints in a differential estimation method that involves several of the constraints seen in the current differential methods, which allows obtaining accurate flows.Considering the importance of understanding the movement of certain structures such as left ventricular and myocardial wall for better medical diagnosis, our main goal is to find an estimation method useful to assist diagnosis tasks in computer tomography images.     

Image fusion technique using multivariate statistical model for wavelet coefficients

Wavelet-based image fusion techniques have been highly successful in combining important features such as edges and textures of source images. In this work, a new discrete wavelet transform (DWT)-based fusion algorithm is proposed using a locally-adaptive multivariate statistical model for the wavelet coefficients of the source images as well as that of the fused image. The multivariate model is proposed based on the fact that the DWT coefficients of source images are correlated not only with each other but also with the fused image. By using this model as a joint prior function, an estimate of the fused coefficients is derived via the Bayesian maximum a posteriori estimation technique. Experimental results show that performance of the proposed fusion method is better than that of the other methods in terms of commonly-used metrics such as structural similarity, peak signal-to-noise ratio, and cross-entropy.     

基于精确模型和逆投影的超大视场红外图像畸变校正

针对超大视场红外图像畸变大、与人眼视觉差异明显的问题,提出了一种基于精确模型和逆投影的超大视场红外图像畸变校正算法以改善其视觉效果.该算法首先利用精确模型对超大视场红外相机成像中的物、像关系进行描述;然后,针对红外图像像素采样率不高的缺点,利用较为精确的三次卷积插值法对图像进行插值来补全成像信息;最后,根据校正图像上的待赋值像点的坐标,结合校正模型和超大视场红外相机精确模型,计算该像点逆投影到插值图像时的对应坐标,并以最近邻像点像素值作为校正后图像像点的赋值.车载道路场景下的超大视场红外图像畸变校正实验结果显示,所提出的算法图像校正结果边界清晰、无锯齿效应,对场景中的直线平均还原偏差小于0.35 pixels,表明该算法对超大视场红外图像畸变校正具有较好的适用性.     

基于天空约束暗通道先验的图像去雾

针对现有暗通道图像去雾算法存在的天空色彩失真,景物边缘光晕效应等问题,本文提出了基于暗通道理论的改进去雾算法.由于暗原色先验理论不适用于天空区域,本文将引导滤波用于天空区域的细化分割,准确估计包含天空区域图像的大气光照强度,解决了天空色彩失真问题;其次,利用中值滤波得到详细边缘信息,进而得到更为清晰的透射率,有效抑制了景物边缘光晕问题;最后针对去雾后图像偏暗的问题,在HSV空间对亮度分量V通道进行增强处理.实验结果表明,针对带雾图像,本文算法能够有效地去雾,改善天空区域色彩失真以及景物边缘光晕问题.     

Unsupervised fisheye image correction through bidirectional loss with geometric prior

Neural network based methods for fisheye distortion correction are effective and increasingly popular, although training network require a high amount of labeled data. In this paper, we propose an unsupervised fisheye correction network to address the aforementioned issue. During the training process, the predicted parameters are employed to correct strong distortion that exists in the fisheye image and synthesize the corresponding distortion using the original distortion-free image. Thus, the network is constrained with bidirectional loss to obtain more accurate distortion parameters. We calculate the two losses at the image level as opposed to directly minimizing the difference between the predicted and ground truth of distortion parameters. Additionally, we leverage the geometric prior that the distortion distribution depends on the geometric regions of fisheye images and the straight line should be straight in the corrected images. The network focuses more on the geometric prior regions as opposed to equally perceiving the whole image without any attention mechanisms. To generate more appealing corrected results in visual appearance, we introduce a coarse-to-fine inpainting network to fill the hole regions caused by the irreversible mapping function using distortion parameters. Each module of the proposed network is differentiable, and thus the entire framework is completely end-to-end. When compared with the previous supervised methods, our method is more flexible and shows better practical applications for distortion rectification. The experiment results demonstrate that our proposed method outperforms state-of-the-art methods on the correction performance without any labeled distortion parameters.     

一种基于虚拟三角形的图像自动配准方法

图像配准是图像处理和分析的关键技术之一.本文提出了一种基于虚拟三角形的图像自动配准方法来处理具有全局刚体变换的图像配准问题.该方法主要分三步:首先采用改进的Harris算子从参考图像和待配准图像中分别提取角点特征,将每三个角点顺次连接起来构成一个虚拟三角形;然后运用刚体变换模型下匹配虚拟三角形对全等的准则找到全等性最好的一对虚拟三角形,利用它们的对应顶点求解刚体变换模型参数的初始值;最后根据刚体变换模型参数的初始值和一个预设的门限得到所有的匹配角点特征,通过它们求得最终的刚体变换模型参数.实际图像实验结果表明:本文提出的图像自动配准方法是正确和有效的,并具有较高的配准精度.