基于多分辨率双边滤波的红外场景杂波抑制

背景杂波是影响红外搜索跟踪系统探测性能的主要因素,针对这一问题,根据红外场景中目标和背景特性,提出了一种基于多分辨率双边滤波的红外场景杂波抑制新方法.首先采用非下采样轮廓波对红外场景图像进行多尺度、多方向分解,提取红外原始场景图像在不同尺度和方向上的细节特征,然后,根据目标和背景信号子带分布特性之差异,通过应用双边滤波调整分解后的各子带系数,最后重构各子带就可将红外场景中目标信号和背景杂波分离,可有效地将背景杂波剔除掉.将本文提出的方法应用于实际的红外场景,实验结果显示,与经典的二维最小均方误差方法相比较,该方法具有更好的杂波抑制能力.     

基于引导滤波与改进PCNN的多聚焦图像融合算法

针对多聚焦图像融合中目标物边缘处产生虚影的问题,提出一种基于引导滤波与改进脉冲耦合神经网络(PCNN)的多聚焦图像融合算法。该算法利用引导滤波器对源图像进行多尺度边缘保持分解,对分解得到的基本图像和细节图像采用不同的引导滤波加权融合策略进行初步融合;将初步融合图作为外部输入激励刺激改进的PCNN模型;根据融合权重图对多幅源图像进行融合,获得最终的融合图像。实验结果表明,与传统融合算法相比,本文方法较好地保留了源图像的边缘、区域边界以及纹理等细节信息,避免了目标物边缘处产生虚影,提高了融合图像的质量。     

基于潜在低秩表示与复合滤波的红外与弱可见光增强图像融合方法

针对传统红外与弱可见光图像融合算法中存在的亮度与对比度低、细节轮廓信息缺失、可视性差等问题,提出一种基于潜在低秩表示与复合滤波的红外与弱可见光增强图像融合方法.该方法首先利用改进的高动态范围压缩增强方法增强可见光图像提高亮度;然后利用基于潜在低秩表示与复合滤波的分解方法分别对红外与增强后的弱可见光图像进行分解,得到相应的低频和高频层;再分别使用改进的对比度增强视觉显著图融合方法与改进的加权最小二乘优化融合方法对得到的低频和高频层进行融合;最后将得到的低频和高频融合层进行线性叠加得到最终的融合图像.与其他方法的对比实验结果表明,用该方法得到的融合图像细节信息丰富,清晰度高,具有良好的可视性.     

基于迭代导向滤波与多视觉权重信息的红外与 可见光图像融合

为了充分利用源图像重要特征,提出了一种基于迭代导向滤波与多视觉权重信息的红外与可见光图像融合算法.首先,通过一种迭代导向滤波器将输入图像分解为基础层与细节层;其次,利用边角信息、清晰度与对比度来综合确定二进制权重系数,再选择导向滤波对其优化,进一步去除噪声并抑制伪影的产生;最后,应用重构准则对基础层与细节层进行组合,得到融合图像.实验结果表明,与其它多尺度分解相比,该方法具有尺度感知特性,可以更好地分离空间重叠的特征,不仅可以使夜视融合图像的细节信息更突出,还能够有效地抑制伪影.     

多波段前视红外图像融合的海面杂乱背景平滑方法

为了有效地克服单波段前视红外图像中存在的点状杂波、条状波浪以及局部高亮区域等随机杂乱背景的影响,开展了基于多波段前视红外图像融合的海面杂乱背景平滑方法的研究。充分利用多波段前视红外图像之间的互补性和差异性,通过融合多波段红外图像的信息,旨在平滑抑制海面杂乱背景并保持舰船目标的特征信息,为舰船目标检测提供一幅优质的图像。首先利用离散小波变换将多波段源图像分解为低频子带和高频子带,其中,高频子带主要包含了图像中背景以及舰船目标的细节信息,低频子带主要包含了图像的亮度以及对比度信息;对于高频子带,在基于高频系数取绝对值最大法得到高频融合图像后,计算每个像素的区域能量来对高频融合图像进行调制以抑制图像背景的细节信息而保留舰船目标的细节信息;对于低频子带,通过平均法融合低频子带并利用导向滤波对低频融合图像进行平滑滤波处理;最后对高频融合图像和低频融合图像进行小波逆变换得到的重构图像即为融合图像。对实际采集的多波段前视红外图像进行仿真实验,将该方法与双边滤波、导向滤波、梯度最小化、相对全变分、双边纹理滤波和滚动滤波共6种图像平滑滤波方法进行对比。结果表明:所提出的方法通过有效地融合多波段图像的信息,将空间域的平滑处理转换到频率域中进行,能够很好地平滑海面随机杂乱背景并较好地保持舰船目标的结构、灰度以及对比度信息,大大增强了舰船目标的可分离性,其图像平滑性能优于作为对比的6种方法。     

基于双边滤波算子的医学脊椎去噪

为了有效滤除医学脊椎模型的噪声点,同时更好地保持模型细节,提出了一种基于双边滤波算子的医学脊椎去噪模型.采用双边滤波在多尺度条件下进行脊椎三维模型轮廓线的提取,设计改进自适应扩散系数,以更好的优化控制整个扩散过程.根据图像的离散特征,建立相应的离散迭代方程,使迭代过程离散化,并设计迭代停止准则,当去噪平滑后的图像模型与噪声相关性最小时停止迭代.与经典的向异性扩散模型方法实验结果相比,本方法在解决去噪方面达到了很好的滤波效果,同时也较好地保持了医学图像的边缘细节特征,大大优于传统滤波算法.     

基于双树复小波与波原子的图像扩散滤波

图像的非线性扩散滤波来源于热方程的思想,其关键在于计算适当的扩散系数和控制扩散方向. 在已有的扩散模型中,由于扩散系数仅依赖于图像的梯度,因而这类模型容易受噪声的干扰;同时,图像的细节信息(如纹理)容易被误认为是噪声而被去除. 为克服这些不足,首先给出了一种采用双树复小波变换计算扩散系数的方法;然后设计了一种用于图像滤波的非线性扩散模型,最后提出了基于双树复小波变换和波原子阈值相结合的图像滤波算法. 仿真结果表明,所提出的算法在对含噪图像滤波的同时,能够较好地保持图像的边缘和纹理等细节信息. 关键词： 图像扩散滤波 非线性扩散 波原子 双树复小波变换     

分级多尺度变换的水下偏振图像融合法

提出了一种分级多尺度融合的水下偏振图像处理方法.首先,利用非负矩阵分解对偏振参量图像进行融合增强,得到所含局部特征信息完整且冗余度低的偏振参量融合图像;在此基础上,基于二维经验模式分解分别将偏振参量融合图像与偏振强度图像进行多尺度变换,对得到的高低频子图像分别进行加权平均融合,融合权重是采用穷举搜索法计算得到;最后,将高低频融合结果反变换得到最终融合图像.实验仿真结果表明该融合方法在增强图像细节信息及提高水下偏振图像对比度方面具有显著效果.     

基于数学形态学和遗传优化的图像去噪

针对图像滤波时损失图像细节这一问题,提出了一种自适应多尺度形态滤波方法,在普通多尺度形态开、闭滤波基础上增加了多尺度top hat变换和bottom hat变换,用于提取并平滑小尺度的图像信息。top hat变换和bottom hat变换的系数对整个滤波器性能起着重要的作用,采用遗传优化的方法对其进行优化。实验结果表明,该方法噪声去除效果好,图像细节保持完整,提高了输出图像的信噪比,增强了滤波器的自适应性和智能性,处理效果明显优于传统滤波方法。     

基于小波变换的激光主动成像图像去噪方法

针对激光主动成像图像的特点,提出了将小波变换和中值滤波相结合的图像去噪方法。在对小波分解后的水平、垂直和对角3个方向高频细节图像进行处理时,我们采用3种不同形状的模板进行均值滤波,为了保护图像的边缘和细节信息,采用边缘检测法来将高频中的边缘细节与噪声分开。实验结果表明:该方法在降低图像噪声的同时又较好地保留了图像的细节,去噪效果比较理想。     

基于照射_反射模型和有界运算的多谱段图像增强

根据多尺度照射_反射模型, 结合广义有界运算模型和引导滤波, 能够有效地解决多谱段降质图像的增强问题. 算法采用自适应的引导滤波核函数作为环绕函数, 估计反映图像整体结构的不同尺度的低频照射分量; 利用有界广义对数比(general log-radio, GLR)模型加法代替Retinex理论中的对数变换运算; 再由GLR模型减法去除照射分量, 将不同尺度的反射分量从原始图像中分割出来; 对不同尺度反射分量的有效信息采用有界GLR模型乘法和加法进行融合, 有效地避免光晕伪影现象及越界现象的发生, 得到多尺度反射分量图像, 即最终的增强图像. 通过对可见光波段的低照度图像和雾霾图像、红外图像、X光医学图像四组多谱段降质图像实验分析, 以对比度和信息熵作为评价指标, 与同类算法进行了图像增强效果的定性和定量对比, 结果表明本文算法增强后的图像纹理和边缘细节更加丰富、对比度更高、视觉效果更佳, 可广泛地应用于多种图像增强领域.     

Infrared and visible image fusion based on visual saliency map and weighted least square optimization

The goal of infrared (IR) and visible image fusion is to produce a more informative image for human observation or some other computer vision tasks. In this paper, we propose a novel multi-scale fusion method based on visual saliency map (VSM) and weighted least square (WLS) optimization, aiming to overcome some common deficiencies of conventional methods. Firstly, we introduce a multi-scale decomposition (MSD) using the rolling guidance filter (RGF) and Gaussian filter to decompose input images into base and detail layers. Compared with conventional MSDs, this MSD can achieve the unique property of preserving the information of specific scales and reducing halos near edges. Secondly, we argue that the base layers obtained by most MSDs would contain a certain amount of residual low-frequency information, which is important for controlling the contrast and overall visual appearance of the fused image, and the conventional “averaging” fusion scheme is unable to achieve desired effects. To address this problem, an improved VSM-based technique is proposed to fuse the base layers. Lastly, a novel WLS optimization scheme is proposed to fuse the detail layers. This optimization aims to transfer more visual details and less irrelevant IR details or noise into the fused image. As a result, the fused image details would appear more naturally and be suitable for human visual perception. Experimental results demonstrate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.     

Fusion of visible and infrared images using saliency analysis and detail preserving based image decomposition

Image fusion for visible and infrared images is a significant task in image analysis. The target regions in infrared image and abundant detail information in visible image should be both extracted into the fused result. Thus, one should preserve or even enhance the details from original images in fusion process. In this paper, an algorithm using pixel value based saliency detection and detail preserving based image decomposition is proposed. Firstly, the multi-scale decomposition is constructed using weighted least squares filter for original infrared and visible images. Secondly, the pixel value based saliency map is designed and utilized for image fusion in different decomposition level. Finally, the fusion result is reconstructed by synthesizing different scales with synthetic weights. Since the information of original signals can be well preserved and enhanced with saliency extraction and multi scale decomposition process, the fusion algorithm performs robustly and excellently. The proposed approach is compared with other state-of the-art methods on several image sets to verify the effectiveness and robustness.     

Image fusion algorithm based on redundant-lifting NSWMDA and adaptive PCNN

Pyramid decomposition in the NSCT transformation is a band-pass filtering process in the frequency domain where different scales of images are orthogonal. However, from the perspective of the image content, correlation is likely to exist between the fused images, and this kind of decomposition makes images of different scales contain redundant information, as a result of which the fused image may not capture the subtle information from the original images. In order to overcome the above-mentioned problem, an effective image fusion method based on redundant-lifting non-separable wavelet multi-directional analysis (NSWMDA) and adaptive pulse coupled neural network (PCNN) has been proposed. The original images are firstly decomposed by using the NSWMDA into several sub-bands in order to retain texture detail and contrast information of the images, and then adaptive PCNN algorithm is applied on the high-frequency directional sub-bands to extract the high-frequency information. The low-frequency sub-bands are evaluated by weighted average based on Gaussian kernel with a chosen maximum fusion rule. Results from experiments show that the proposed method can make the fused image maintains more texture details and contrast information.     

Variational infrared image enhancement based on adaptive dual-threshold gradient field equalization

Infrared images are characterized by low signal to noise ratio (SNR) and fuzzy texture edges. This article introduces the variational infrared image enhancement algorithm based on gradient field equalization with adaptive dual thresholds. Firstly, we transform the image into gradient domain and get the gradient histogram. Then, we do the gradient histogram equalization. By setting adaptive dual thresholds to qualify the gradients, the image is prevented from over enhancement. The total variation (TV) model is adopted in the reconstruction of the enhanced image to suppress noise. It is shown from experimental results that the image edge details are significantly enhanced, and therefore the algorithm is qualified for enhancement of infrared images in different applications.     

A gray-natural logarithm ratio bilateral filtering method for image processing

A new method based on gray-natural logarithm ratio bilateral filtering is presented for image smoothing in this work. A new gray-natural logarithm ratio range filter kernel, leading to adaptive magnitude from image gray distinction information, is pointed out for the bilateral filtering. The new method can not only well restrain noise but also keep much more weak edges and details of an image, and preserve the original color transition of color images. Experimental results show the effectiveness for image denoising with our method.     

基于光照-反射成像模型和形态学操作的多谱段图像增强算法

为解决多谱段降质图像增强问题,提出了一种基于光照-反射成像模型和形态学操作的多谱段图像增强算法.首先对图像饱和度使用自适应非线性拉伸函数进行拉伸,使增强后的图像色彩更加饱和、自然;接下来利用引导滤波算法提取出图像的光照分量,提出了一种基于细节特征的加权融合策略,利用光照分布特性构造了一种自适应Gamma校正函数对光照分量进行处理,并将其与利用对比度受限的自适应直方图均衡化方法处理后的光照分量以及原始光照分量进行融合;然后在反射分量校正时,构造了一种形态学操作函数来校正反射信息;最后合并光照分量和反射分量,并与处理后的饱和度分量和色调分量一起得到增强图像.采用主客观评价指标对可见光低照度图像、水下图像、高动态范围图像、沙尘暴图像、雾天图像和热红外图像6种降质多谱段图像实验结果进行分析比较,结果表明本文算法能够有效地抑制图像噪声、增强图像细节信息、改善图像视觉效果,可应用于多种图像增强领域.     

A New X-ray Medical-Image-Enhancement Method Based on Multiscale Shannon–Cosine Wavelet

Because of noise interference, improper exposure, and the over thickness of human tissues, the detailed information of DR (digital radiography) images can be masked, including unclear edges and reduced contrast. An image-enhancement algorithm based on wavelet multiscale decomposition is proposed to address the shortcomings of existing single-scale image-enhancement algorithms. The proposed algorithm is based on Shannon–Cosine wavelets by taking advantage of the interpolation, smoothness, tight support, and normalization properties. Next a multiscale interpolation wavelet operator is constructed to divide the image into several sub-images from high frequency to low frequency, and to perform different multi-scale wavelet transforms on the detailed image of each channel. So that the most subtle and diagnostically useful information in the image can be effectively enhanced. Moreover, the image will not be over-enhanced and combined with the high contrast sensitivity of the human eye’s visual system in smooth regions, different attenuation coefficients are used for different regions to achieve the purpose of suppressing noise while enhancing details. The results obtained by some simulations show that this method can effectively eliminate the noise in the DR image, and the enhanced DR image detail information is clearer than before while having high effectiveness and robustness.     

Fusion of the low-light-level visible and infrared images for night-vision context enhancement

For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement(FNCE) method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details(edges), the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.