基于时域矩匹配的红外图像自适应非均匀性校正EI北大核心CSCD

非制冷红外焦平面阵列的响应非均匀性通常表现出与条带噪声相类似的特性。基于场景的校正算法是提高图像质量,补偿响应特性漂移的有效措施。在深入研究矩匹配理论的基础之上,提出了一种新的时域矩匹配非均匀性校正方法。利用相邻帧矩匹配后的图像对场景的变化列进行估计,并在时间域对校正参数进行自适应更新。利用真实的红外图像序列验证了该算法在收敛速度和去鬼影方面的优越性和有效性。     

基于时域矩匹配的红外图像自适应非均匀性校正

非制冷红外焦平面阵列的响应非均匀性通常表现出与条带噪声相类似的特性。基于场景的校正算法是提高图像质量,补偿响应特性漂移的有效措施。在深入研究矩匹配理论的基础之上,提出了一种新的时域矩匹配非均匀性校正方法。利用相邻帧矩匹配后的图像对场景的变化列进行估计,并在时间域对校正参数进行自适应更新。利用真实的红外图像序列验证了该算法在收敛速度和去鬼影方面的优越性和有效性。     

时空域非线性滤波红外序列图像非均匀性校正

针对现有非均匀性校正方法中校正结果收敛速度慢、图像退化、存在"鬼影"现象等问题,提出一种空域三边滤波与时域梯度加权均值滤波相结合的红外序列图像非均匀性校正方法.该方法首先利用三边滤波将图像分解为基本分量与细节分量;然后对分离出的细节分量序列图像的时域曲线进行梯度加权均值滤波,从而分离出细节分量中非均匀性和场景细节;最后用原图减去非均匀性,得到校正结果.实验结果表明,该方法能有效地抑制"鬼影"并改善图像退化现象,校正结果无论主观视觉还是客观评价指标均明显优于时域高通与神经网络校正方法.     

基于焦平面归一化响应特性的红外非均匀性校正

红外焦平面阵列的非均匀性噪音是制约红外成像质量的主要因素,非均匀性校正是红外焦平面器件应用的一个关键技术.本文提出了一种基于焦平面归一化响应特性且易于实现的非均匀性校正算法,并基于像元分布的卡方直方图提出一种新的图像非均匀性评估方法,即校正比例.该方法的校正输出考虑了每个像元的观测值与焦平面的响应信号的平均值,校正参量通过将像元的输出与其理想校正结果之间的偏差用焦平面响应的平均值建立联系而计算得到.提出的校正比例兼顾考虑了焦平面响应的时间与空间特性,比现有的图像非均匀性评估方法更能合理衡量焦平面的非均匀性程度.多种非均匀性校正的评价数据以及实验结果表明,该算法的校正效果优于两点校正法与原值拟合二阶校正法,并对于响应异常的像元具有较强的校正能力.此外其校正准确度高,所需参量少,易于实时处理,具有较强的实用价值.     

基于自适应扩散模型的单帧红外条纹非均匀性校正算法

针对红外焦平面成像系统存在列向条纹非均匀性的现象,采用了一种基于自适应PM扩散模型的非均匀校正新算法。首先,综合利用图像梯度信息和局部灰度统计信息,自适应计算PM模型的扩散阈值;然后将每列像素的PM模型估计值作为该列像素的期望值;最后采用最陡下降法迭代计算得到每列像元的校正参数,并对结果进行循环校正以提高校正效果。实验结果表明:该算法可以保护图像边缘信息,与同类算法相比,能够更有效地抑制条纹非均匀性,并且能够防止图像产生鬼影。     

多通道宽响应域TDI CCD成像系统的非均匀性校正

针对时间延迟积分(TDI)电荷耦合器件(CCD)成像系统多通道间存在响应非均匀问题,开展了基于辐射定标的校正方法研究。结合成像系统的信号处理流程,推导了响应非均匀性校正的理论模型,提出了一种基于加权最小二乘的定标校正方法,分别在1/4、1/2和3/4饱和响应处采集三组图像数据,并利用高斯加权函数对三组通道间的校正参数进行加权最小二乘拟合,从而获得优化的校正参数。利用TDI CCD成像系统进行实验验证,结果表明,系统的响应非均匀性(1/2饱和响应处)由原来的4.09%降低至0.85%,并优于两点校正法的1.93%,同时该方法在宽响应域内均获得了良好的校正效果,具有精度高、实用性强等特点,可应用于机载、星载等相机载荷,为获取高质量的遥感图像数据奠定技术基础。     

推扫型干涉成像光谱仪去除条带非均匀性的方法

分析了推扫型干涉成像光谱仪的结构特点和工作原理,指出了由于加工误差导致入射狭缝的宽度不均匀,使干涉图像沿狭缝方向存在亮度差异,复原后的光谱立方体图像上出现平行于推扫方向的非均匀性条带,影响了光谱立方体的图像质量和光谱精确度.采取校正系数法去除条带,讨论了获取校正系数的方法,并使用仪器的定标干涉数据提取成像面上沿狭缝方向的不均匀特性,获得了校正系数,对干涉图像进行处理以消除条带.结果表明:校正系数法能够去除大部分条带.根据处理后残余条带的情况,在没有在轨定标数据的情况下,使用均匀景物的光谱立方体数据提取不均匀特性,修改了校正系数,有效地消除了残余条带.     

基于多尺度分析和加权最小二乘法的非制冷红外条纹噪声校正算法

为了提高红外成像质量的同时更大程度地保持纹理信息,提出一种多尺度分析和加权最小二乘法的条纹噪声非均匀性校正算法.该算法利用加权最小二乘法对图像进行平滑,应用小波变换提取平滑图像的垂直分量,并将其垂直分量替换为原始图像的垂直分量,利用小波重构输出校正后的图像.算法能够精准地去除红外噪声,而不会带来更加麻烦的"鬼影"问题.用该算法对多组不同红外图像数据进行仿真实验,并与其他先进的红外条纹非均匀校正算法进行对比分析,结果表明所提算法校正结果有较好的视觉效果和图像质量评估参数.     

基于焦平面归一化响应特性的红外非均匀性校正

红外焦平面阵列的非均匀性噪音是制约红外成像质量的主要因素,非均匀性校正是红外焦平面器件应用的一个关键技术.本文提出了一种基于焦平面归一化响应特性且易于实现的非均匀性校正算法,并基于像元分布的卡方直方图提出一种新的图像非均匀性评估方法,即校正比例.该方法的校正输出考虑了每个像元的观测值与焦平面的响应信号的平均值,校正参量...     

基于场景的红外焦平面阵列非均匀性校正算法综述

红外焦平面阵列的非均匀性噪声是制约红外成像质量的主要因素。基于场景的非均匀校正算法通常利用图像序列并依赖帧间运动对焦平面阵列的非均匀性进行校正。介绍和分析了全局非均匀性校正,Kalman滤波器法,自适应滤波法,轨迹跟踪法,基于场景运动分析的校正算法,基于小波变换实现低通滤波的校正算法,高通滤波与神经网络相结合的算法,基于小波去噪、序列图像配准和正交最小二乘拟合的校正算法,改进的神经网络算法以及代数算法等,对算法进行了比较。     

Adaptive grayscale adjustment-based stripe noise removal method of single image

An effective adaptive grayscale adjustment-based stripe noise removal method of single image is presented. In this method, we extract all stripe noise spectra from each frame, and then exclude relatively stationary images by sub-pixel registration to obtain continuously moving image sequences. By accumulating the same frequency spectra of the image sequences, we acquire accurate stripe noise spectra. Using the proportion of each stripe noise spectrum, we calculate the new histogram of the current column image, thereby effectively diminishing all frequency noises. In using the histogram for grayscale processing, we adopt the coefficient weight of the bilateral filtering function. Through intensity and distance factors, this function controls the ratio of the column histograms included in the calculation of the new current column histogram. This prevents the production of artifacts in the proposed method. Experiments demonstrate that our algorithm efficiently removes stripe noise and exhibits better performance than do the other algorithms discussed in literature.     

Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching

A method of contrast enhancement of optical coherence tomography (OCT) images based on least squares fitting and histogram matching is presented. Several different functions are adopted as the probability density functions of the gray levels to fit the normalized histogram of an OCT image and histogram matching is used to enhance the OCT image automatically. The effectiveness of the method is proved by the experimental results.     

Two-dimensional Otsu's thresholding segmentation method based on grid box filter

Otsu algorithm, an automatic thresholding method, is widely used in classic image segmentation applications. In this paper, a novel two-dimensional (2D) Otsu thresholding algorithm based on local grid box filter is proposed. In our method, firstly by utilizing the coarse-to-fine idea, the 2D histogram is divided into regions by grid technique, and each region is used as a point to form a new 2D histogram, to which 2D Otsu thresholding algorithm and an improved particle swarm optimization (PSO) algorithm are applied to get the region number of the new 2D histogram threshold. Then on the result region, the mean of the 2D histogram is computed base on box filter, and the two algorithms are applied again to obtain the final threshold for the original image. Experimental results on real data show that the proposed algorithm gets better segmentation results than the traditional recursion Otsu algorithm. It significantly reduces the time of segmentation process and simultaneously has the higher segmentation accuracy.     

优化Savitzky-Golay滤波器的参数及其在傅里叶变换红外气体光谱数据平滑预处理中的应用

对傅里叶变换中红外光谱数据的平滑预处理中,通常采用Savitzky-Golay滤波器的方法进行光谱数据的平滑预处理,然而Savitzky-Golay滤波器的多项式拟合阶次和窗宽等参数的合理选则始终是一个难题,并无统一的选择依据,通常在一定数值范围内,采用多组数据进行遍历尝试,最终选择一组相对较优的数据作为Savitzky-Golay滤波器的多项式拟合阶次和窗宽参数。文中探索了Savitzky-Golay滤波器的多项式拟合阶次和窗宽等参数的优化选取这一问题,并对其主要频率指标参数与多项式拟合阶次和窗宽等参数进行了定性定量分析,得出了截至频率、阻带起始点频率、第一旁瓣峰值频率及第一旁瓣峰值幅度与窗宽和阶次之间具体的计算方程表达式。随后,根据采集的中红外气体组分的光谱数据特征,依据上述计算方程式,优化计算选取多项式拟合阶次和窗宽分别为8和11时,其Savitzky-Golay滤波器的中红外气体组分的光谱数据平滑效果最优。最后通过实际采集的0.1%, 0.2%, 0.5%, 1%, 2%, 5%浓度的CH₄光谱数据进行平滑预处理,在次吸收峰区域,原始光谱的折算吸光率相对最大误差和最小误差分别为17.230 5%和0.243 0%, 平滑处理后的光谱的折算吸光率相对最大误差和最小误差分别为0.088 0%和0.020 6%。可见经过Savitzky-Golay滤波器进行所探索的光谱数据预处理之后其相对误差基本稳定,并且相对较低,为后期光谱数据的准确定性和定量分析奠定了基础。     

一种用于计算三维视觉测量中线结构光平面的新型算法

介绍了一种基于线结构光的机器视觉测量系统的光平面计算方法.该方法采用图像减影来获取光条图像,利用Steger算法提取图像中的亚像素光条中心点,再用正交直线拟合法计算图像坐标系下的光条直线方程.通过靶标特征点的世界坐标和交比不变性,计算线结构光在靶标平面上的世界坐标点,并将这些坐标点用正交平面拟合法计算得到光平面方程.为了提高整体准确度,本文对算法进行了细节优化,给出了标定系统的设计方案和实验过程.实验结果表明,该方法具有较好的鲁棒性和较高的准确度.     

Stripe noise removal for infrared image by minimizing difference between columns

This paper introduces a novel approach to eliminate stripe noise in infrared images. The differences between bias voltages in column readout circuit of uncooled infrared sensors result in strong stripe noise which changes slowly in time. The problem can be solved by estimating the bias of each column of infrared images and correcting infrared images with the estimated biases. The bias estimation is translated into an energy optimization problem in the paper. The optimization aims to minimize difference between neighboring columns of images. Our approach can be processed on a single image, or in a recursive way in order to significantly reduce the computation in one frame time. Our approach is compared to the state-of-the-art the stripe noise removal method using realistic infrared images, and the experimental results show the effectiveness and efficiencies of our proposed approach.     

Reconstruction of 3D ultrasound images based on Cyclic Regularized Savitzky-Golay filters

This paper presents a new three-dimensional (3D) ultrasound reconstruction algorithm for generation of 3D images from a series of two-dimensional (2D) B-scans acquired in the mechanical linear scanning framework. Unlike most existing 3D ultrasound reconstruction algorithms, which have been developed and evaluated in the freehand scanning framework, the new algorithm has been designed to capitalize the regularity pattern of the mechanical linear scanning, where all the B-scan slices are precisely parallel and evenly spaced. The new reconstruction algorithm, referred to as the Cyclic Regularized Savitzky-Golay (CRSG) filter, is a new variant of the Savitzky-Golay (SG) smoothing filter. The CRSG filter has been improved upon the original SG filter in two respects: First, the cyclic indicator function has been incorporated into the least square cost function to enable the CRSG filter to approximate nonuniformly spaced data of the unobserved image intensities contained in unfilled voxels and reduce speckle noise of the observed image intensities contained in filled voxels. Second, the regularization function has been augmented to the least squares cost function as a mechanism to balance between the degree of speckle reduction and the degree of detail preservation. The CRSG filter has been evaluated and compared with the Voxel Nearest-Neighbor (VNN) interpolation post-processed by the Adaptive Speckle Reduction (ASR) filter, the VNN interpolation post-processed by the Adaptive Weighted Median (AWM) filter, the Distance-Weighted (DW) interpolation, and the Adaptive Distance-Weighted (ADW) interpolation, on reconstructing a synthetic 3D spherical image and a clinical 3D carotid artery bifurcation in the mechanical linear scanning framework. This preliminary evaluation indicates that the CRSG filter is more effective in both speckle reduction and geometric reconstruction of 3D ultrasound images than the other methods.     

高噪声背景下激光条纹亚像素中心的提取

针对线结构光三维形貌测量中大量噪声易干扰激光条纹中心提取准确度的问题,提出了一种条纹亚像素中心提取方法。分析条纹图像中的噪声,采用平均法和中值滤波预处理图像;利用迭代阈值分割及形态学方法,获取条纹目标,引入距离变换提取条纹的像素级中心;根据像素级中心、二值信息及光强灰度,结合曲线拟合及重心法精确提取条纹的亚像素中心。仿真分析和实验验证下,相邻行条纹中心列坐标最大偏差值像素小于2 ,平均偏差像素值约为0.3,与传统方法相比,2项指标值更小。实验结果表明,算法有效利用条纹灰度分布规律,可降低噪声对中心定位精度的影响,更逼近条纹真实中心位置,抗噪能力极强。     

Robust visual tracking base on adaptively multi-feature fusion and particle filter

In order to avoid the tracking failure based on single feature under the conditions of cluttered backgrounds illumination changes, a robust tracking algorithm was proposed based on adaptively multi-feature fusion and particle filter. Color histogram was used to describe the overall distribution characteristics of the target and histogram of oriented gradients containing some construction information and LBP is very effective to describe the image texture features. The Three features were fused in the frame of particle filter. Meanwhile, the weights of each feature were adjusted dynamically. The experimental results show that with adaptive fusion, the tracker becomes more robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion.