一种基于小波变换的红外偏振融合算法

针对红外偏振图像可以较好地抑制背景噪声,对目标边缘信息比较敏感的特点,提出一种基于小波变换的红外偏振融合算法,它主要用于红外辐射强度图像和偏振度图像融合,增加图像的信息量。首先采用小波变换对参与融合的每幅图像分别进行各尺度分解,得到各尺度小波系数,然后针对不同尺度小波系数,采用邻域平均梯度为判据进行融合,得到融合后的各尺度小波系数,最后通过小波逆变换进行图像重构,得到融合图像。融合前后的图像对比表明融合图像在保留辐射强度图像的清晰度的同时,突出了目标的边缘、轮廓信息。相对于辐射强度图像,融合图像的梯度均值提高了112%,相对于偏振度图像,融合图像的标准差提高了151%,信息熵提高了38%。     

一种红外偏振图像的二次融合方法

根据红外偏振特性图像的冗余性与互补性,提出了一种基于梯度特征和支持度变换的二次融合方法。利用提出的方法先对红外偏振度与偏振角图像进行梯度特征融合,然后利用支持度变换对梯度特征融合图像和合成强度图像进行二次融合,得到了边缘突出、轮廓清晰、对比度高的的融合图像。实验结果表明:融合图像与偏振角图像、偏振度图像、合成强度图像相比局部方差分别提高23.02%、176.9%、148.2%;对比度分别提高67.84%、196.5%、49.39%;平均梯度分别提高46.09%、164.1%、214.5%。     

暗弱目标偏振度图像与偏振角图像去噪方法研究

针对暗弱目标偏振融合图像存在背景噪声强、细节轮廓特征不明显等问题,利用偏振度图像中高斯背景噪声灰度与待测目标灰度的差异性,提出了基于噪声模板阈值匹配的偏振度图像去噪算法。分析了偏振角图像中纯噪声区域子图像与目标轮廓区域子图像的灰度相关性,提出了基于区域灰度相关性匹配的偏振角图像去噪算法,与传统去噪方法相比,该算法可有效去除大量背景噪声,更好地保留目标轮廓特征。偏振图像融合对比实验结果进一步证明,提出的偏振图像去噪方法可显著提高暗弱目标在图像中的对比度,改善偏振融合增强图像的质量。     

基于结构张量的变分多源图像融合

提出了可以保持源图像特征和细节信息的基于结构张量的变分多源图像融合算法. 首先叙述基于结构张量的融合梯度场, 然后测量每幅源图像的特征图, 根据特征图为源图像的每个梯度构造一个权值, 将携带明显特征的梯度在融合的梯度场中凸显出来, 从而使源图像的特征和细节得到保持, 最后应用变分偏微分方程理论从目标梯度场重建出融合的图像. 实验结果表明, 本文算法融合图像的灰度平均梯度和信息熵均高于小波变换算法、塔分解法和直接梯度融合算法, 视觉效果上,融合图像很好的保留了源图像的特征和细节, 为图像目标检测和识别提供了高质量的图像信息. 关键词： 图像融合 梯度场 结构张量 变分法     

多尺度奇异值分解的偏振图像融合去雾算法与实验

针对现有偏振去雾算法鲁棒性不强和图像增强效果有限的问题,提出一种基于多尺度奇异值分解的图像融合去雾算法。首先,利用偏振测量信息的冗余特性,采用最小二乘法,提高了传统偏振图像去雾算法中偏振信息的准确度;然后,从传统偏振图像去雾算法的局限性出发,定性分析了偏振图像融合去雾的可行性,并提出了一种基于多尺度奇异值分解的偏振图像融合去雾算法;最后,设计了不同能见度条件下的验证实验并进行了量化评价。结果表明,与经典偏振图像去雾算法相比,该算法不需要进行人工参数调节,具有较强的自适应性和鲁棒性,能够有效改善传统算法中出现的光晕以及天空区域过曝的问题,图像信息熵与平均梯度最大可分别提高18.9%和38.4%,有效地增强了复杂光照条件下的视觉成像质量,具有较广泛的应用前景。     

基于稀疏特征的红外与可见光图像融合

针对传统的红外与可见光图像融合算法提取目标信息不突出的问题,提出一种基于非下采样剪切波变换和稀疏结构特征的融合方法.首先用非下采样剪切波变换分解源图像;然后通过主成分分析提取低频子带系数中边缘和轮廓等显著特征,引导低频成分融合规则的设计,同时基于结构信息的稀疏性指导融合高频子带系数;最后经过非下采样剪切波变换逆变换得到融合后的图像.实验结果表明,该方法在保留可见光图像背景信息的基础上,突显了红外图像的结构信息,有效提高了融合效果.     

基于梯度阈值自适应处理的红外图像超分辨率重建

超分辨率图像重建中,Huber马尔可夫随机场模型是一种常用的正则化算子.针对Huber函数中固定梯度阈值引起图像重建效果不佳的问题,本文提出一种梯度阈值自适应处理的红外图像超分辨率重建算法.在最大后验概率理论框架下,构造了基于数据项和正则项的正则化模型;通过迭代的方式,利用中间重建结果不断更新正则化参量,解决了Huber马尔可夫随机场模型中梯度阈值不易选择的难题.实验结果表明,改进算法能够根据局部梯度特征自适应选择相应的正则化参量并找到最优解,较好恢复目标细节的同时有效抑制了图像噪音.     

基于多特征融合的复杂背景下弱小多目标检测和跟踪算法

针对红外弱小多目标的检测和跟踪难题,提出一种基于多特征融合的复杂背景下弱小多目标检测和跟踪算法.融合红外弱小运动目标的灰度特征、梯度特征、运动特征等多个典型特性,进行复杂背景下弱小多目标的检测和跟踪.实验证明:该算法应用于复杂背景下低信噪比的红外弱小多目标图像序列能得到较理想的结果,算法检测概率高、检测速度快、具有较强鲁棒性.     

基于梯度阈值自适应处理的红外图像超分辨率重建

超分辨率图像重建中,Huber马尔可夫随机场模型是一种常用的正则化算子.针对Huber函数中固定梯度阈值引起图像重建效果不佳的问题,本文提出一种梯度阈值自适应处理的红外图像超分辨率重建算法.在最大后验概率理论框架下,构造了基于数据项和正则项的正则化模型;通过迭代的方式,利用中间重建结果不断更新正则化参量,解决了Huber马尔可夫随机场模型中梯度阈值不易选择的难题.实验结果表明,改进算法能够根据局部梯度特征自适应选择相应的正则化参量并找到最优解,较好恢复目标细节的同时有效抑制了图像噪音.     

改进Canny算法的红外人脸图像边缘轮廓提取

红外人脸图像的边缘轮廓特征对于红外人脸检测、识别等相关应用具有重要价值。针对红外人脸图像边缘轮廓提取时存在伪边缘的问题,提出了一种改进Canny算法的红外人脸图像边缘轮廓提取方法。首先通过对引导滤波算法引入“动态阈值约束因子”替换原始算法中的高斯滤波,解决了原始算法滤波处理不均匀和造成红外人脸图像弱边缘特征丢失的弊端;接着对原始算法的非极大值抑制进行了改进,在原始计算梯度方向的基础上又增加了4个梯度方向,使得非极大值抑制的插值较原始算法更加精细;最后改进OTSU(大津)算法,构造灰度-梯度映射函数确定最佳阈值,解决了原始算法人为经验确定阈值的局限性。实验结果表明:提出的改进Canny算法的红外人脸轮廓提取方法滤波后的图像,相较于原始Canny算法滤波处理,信噪比性能提升了34.40%,结构相似度性能提升了21.66%;最终的红外人脸边缘轮廓提取实验的优质系数值高于对比实验的其他方法,证明改进后的算法对于红外人脸图像边缘轮廓提取具有优越性。     

一种分焦平面偏振成像系统光源标定方法北大核心CSCD

水下偏振成像技术是目前水下成像研究的热点,由于自然光在水中衰减大,水下成像系统多采用主动照明方式。针对分焦平面偏振成像系统中偏振照明光源与偏振探测像元偏振方向不匹配引起采集图像偏振信息存在的偏差,进而影响目标图像增强质量的问题,提出了一种分焦平面偏振成像系统光源标定方法。阐述了偏振光源的标定原理,然后给出偏振光源标定的实施步骤,最后采用偏振去雾算法和图像质量评价方法对标定前后的水下目标图像进行了图像增强和图像质量评价。评价结果表明,标定后的增强图像质量优于未标定的增强图像质量,平均梯度最大提升了2.48倍。该标定方法简单有效,实用性强,适用于分焦平面偏振成像系统偏振光源标定。     

Single infrared image super-resolution combining non-local means with kernel regression

In many infrared imaging systems, the focal plane array is not sufficient dense to adequately sample the scene with the desired field of view. Therefore, there are not enough high frequency details in the infrared image generally. Super-resolution (SR) technology can be used to increase the resolution of low-resolution (LR) infrared image. In this paper, a novel super-resolution algorithm is proposed based on non-local means (NLM) and steering kernel regression (SKR). Based on that there are a large number of similar patches within an infrared image, NLM method can abstract the non-local similarity information and then the value of high-resolution (HR) pixel can be estimated. SKR method is derived based on the local smoothness of the natural images. In this paper the SKR is used to give the regularization term which can restrict the image noise and protect image edges. The estimated SR image is obtained by minimizing a cost function. In the experiments the proposed algorithm is compared with state-of-the-art algorithms. The comparison results show that the proposed method is robust to the noise and it can restore higher quality image both in quantitative term and visual effect.     

Nonlocal regularization based on DT-CWT for WMSN video image iterative reconstruction

The prior knowledge of signal is the previous condition of image compressed sensing reconstruction. In order to improve the quality of the priors except for image sparsity, this paper proposes a new model of video image reconstruction. The texture is the important visual feature of video image as a result of its repeat, leading to image global geometrical structures. The nonlocal idea comes from image self-familiar and can represent image detail features from the geometrical point of view. Therefore, the texture geometrical feature of video image is researched, and we take advantage of dual-tree complex wavelet transform to portray the sparsity representation regularization of the texture. What is more, global constrained regularization is constructed with the help of the nonlocal idea. On the basis of the two regularizations above, a new reconstruction model of video image compressed sensing is proposed, which not only preserves the sparsity prior knowledge of image but also improves the quality of prior knowledge of image by promoting geometrical structure. Iterative shrinkage thresholding algorithm is adopted to solve the model leading to a both simple and quick iterative algorithm. Numerical experiments show that our method is efficient for video image recovery, especially preserving the global details of the original video image.     

A categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference features

The fusion of infrared polarization and intensity image can significantly improve the detection performance of target, and the fused image is more suitable for human visual perception and further image-processing tasks. In this paper, a new categorization method of infrared polarization and intensity image fusion algorithm based on the transfer ability of difference feature is proposed. Firstly, the difference feature between two kinds of image and the characteristics of different fusion algorithms are analyzed and summarized. Second, an evaluation vector of fusion algorithm for difference feature transform ability is constructed. Thirdly, the transfer ability of fusion algorithm for difference feature is estimated by the evaluation vector, and the degree of transfer ability of fusion algorithm for difference feature is analyzed. Finally the fusion algorithms are classified by the degree of transfer ability of fusion algorithm for difference feature. The results shows that the proposed fusion algorithm categorization method helps select fusion algorithms in actual scene.     

Fusion for visible and infrared images using visual weight analysis and bilateral filter-based multi scale decomposition

Fusion for visible and infrared images has been an important and challenging work in image analysis. Both the feature information in infrared image and abundant detail information in visible image should be preserved and enhanced in fused result. In this paper, a detail enhanced fusion algorithm through visual weight analysis based on smooth-inspired multi scale decomposition is proposed. With variable parameter, bilateral filter-based idea successfully decomposes the two source image into several scales. At each scale level, visual weight map is calculated and used for fusion. Finally, those levels are synthetized with proper weights. Using this idea, the detail information could be enhanced easily. The experimental results demonstrate the proposed approach performs better than other methods, especially in visual effect and keeping details.     

一种基于特征分析的偏振图像融合方法

偏振成像探测能反映出传统光学成像所无法反映的物体的信息,为了克服计算偏振参量图像丢失细节信息的不足,在已有偏振图像融合方法的基础上,提出一种基于综合图像特征的融合方法。相较于已有算法突出图像的某一方面特征,该算法提取图像的灰度特征、纹理特征和形状特征,据此确定融合权值,对图像进行融合,能够较好地反映目标的细节信息,融合后的图像相较于普通光强图像,方差、信息熵分别提高了12.6%、17.5%,平均梯度从0.59提高到1.83。针对该方法用到的特征维数较高的问题,提供了一种简化算法,耗时从3054降至1337。     

基于结构相似度的图像融合质量评价

在分析现有图像融合质量评价方法特点的基础上，提出了新型的、基于结构相似度的图像融合质量评价方法.针对不同情况，分别采用平均结构相似度、加权平均结构相似度、结构信息与交互信息量之乘积作为图像融合质量客观评价标准.该方法充分考虑了图像的结构信息和人类视觉系统的特性，可以为不同场合下选择不同的算法提供依据.对不同融合算法的质量评价结果表明,该方法是一种有效的图像融合质量评价方法.     

Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules

Infrared polarization and intensity imagery provide complementary and discriminative information in image understanding and interpretation. In this paper, a novel fusion method is proposed by effectively merging the information with various combination rules. It makes use of both low-frequency and high-frequency images components from support value transform (SVT), and applies fuzzy logic in the combination process. Images (both infrared polarization and intensity images) to be fused are firstly decomposed into low-frequency component images and support value image sequences by the SVT. Then the low-frequency component images are combined using a fuzzy combination rule blending three sub-combination methods of (1) region feature maximum, (2) region feature weighting average, and (3) pixel value maximum; and the support value image sequences are merged using a fuzzy combination rule fusing two sub-combination methods of (1) pixel energy maximum and (2) region feature weighting. With the variables of two newly defined features, i.e. the low-frequency difference feature for low-frequency component images and the support-value difference feature for support value image sequences, trapezoidal membership functions are proposed and developed in tuning the fuzzy fusion process. Finally the fused image is obtained by inverse SVT operations. Experimental results of visual inspection and quantitative evaluation both indicate the superiority of the proposed method to its counterparts in image fusion of infrared polarization and intensity images.