A fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform

This paper presents a fusion method for infrared–visible image and infrared-polarization image based on multi-scale center-surround top-hat transform which can effectively extract the feature information and detail information of source images. Firstly, the multi-scale bright (dark) feature regions of source images at different scale levels are respectively extracted by multi-scale center-surround top-hat transform. Secondly, the bright (dark) feature regions at different scale levels are refined for eliminating the redundancies by spatial scale. Thirdly, the refined bright (dark) feature regions from different scales are combined into the fused bright (dark) feature regions through adding. Then, a base image is calculated by performing dilation and erosion on the source images with the largest scale outer structure element. Finally, the fusion image is obtained by importing the fused bright and dark features into the base image with a reasonable strategy. Experimental results indicate that the proposed fusion method can obtain state-of-the-art performance in both aspects of objective assessment and subjective visual quality.     

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.     

Image enhancement through contrast enlargement using the image regions extracted by multiscale top-hat by reconstruction

An image enhancement algorithm based on multiscale top-hat by reconstruction is proposed in this paper. Firstly, multiscale top-hat by reconstruction using multiscale structuring elements is discussed. Then, multiscale bright and black image regions are extracted. Thirdly, useful image regions for image enhancement are obtained from the extracted multiscale bright and black image regions. Finally, after a base image is calculated from the results of the opening and closing by reconstruction operations, the original image is enhanced through combing the useful image regions into the base image. Experimental results on different types of images show that the proposed algorithm is efficient.     

Fusion of infrared-visible images using improved multi-scale top-hat transform and suitable fusion rules

Integration of infrared and visible images is an active and important topic in image understanding and interpretation. In this paper, a new fusion method is proposed based on the improved multi-scale center-surround top-hat transform, which can effectively extract the feature information and detail information of source images. Firstly, the multi-scale bright (dark) feature regions of infrared and visible images are respectively extracted at different scale levels by the improved multi-scale center-surround top-hat transform. Secondly, the feature regions at the same scale in both images are combined by multi-judgment contrast fusion rule, and the final feature images are obtained by simply adding all scales of feature images together. Then, a base image is calculated by performing Gaussian fuzzy logic combination rule on two smoothed source images. Finally, the fusion image is obtained by importing the extracted bright and dark feature images into the base image with a suitable strategy. Both objective assessment and subjective vision of the experimental results indicate that the proposed method is superior to current popular MST-based methods and morphology-based methods in the field of infrared-visible images fusion.     

基于支持度变换和top-hat分解的双色中波红外图像融合

为了解决用多尺度top-hat分解法融合双色中波红外图像时经常存在对比度提升有限、边缘区域失真较重的问题,提出了基于支持度变换和top-hat分解相结合的融合方法。先用支持度变换法将双色中波图像分解为低频图像和支持度图像序列;再从最后一层低频图像中用多尺度top-hat分解法提取各自的亮信息和暗信息;用灰度值取大法分别融合亮信息和暗信息;通过灰度值归一化和高斯滤波分别增强亮、暗信息融合图像;然后融合两低频图像和亮、暗信息增强图像;将融合图像作为新的低频图像和用灰度值取大法融合得到的支持度融合图像序列进行支持度逆变换,得到最终融合图像。该方法的实验结果同采用单一的支持度变换法融合和多尺度top-hat分解法融合相比,融合图像的对比度提升了11.69%,失真度降低了63.42%,局部粗糙度提高了38.12%。说明提出的从低频图像提取亮暗信息,并经过分别融合、增强,再与低频图像进行融合,能有效破解红外融合图像对比度提升和边缘区域失真度降低之间的矛盾,为提高图像融合质量提供了新方法。     

Multiple linear feature detection through top-hat transform by using multi linear structuring elements

A multiple linear feature detection algorithm through top-hat transform using the constructed multiple linear structuring elements is proposed in this paper. The desired linear features are treated as a set. And, the set is divided into different subsets. Multi linear structuring elements corresponding to different subsets are constructed. After that, top-hat transform is performed by using the constructed linear structuring elements, and the results are combined to reconstruct the desired linear features. Then, the extracted linear features are binarized and processed to form the final detected binary linear features. Because of the effective performance of the top-hat transform using the constructed multi linear structuring elements, the linear features of different images from different applications could be well detected. The analysis and experimental results show that, the proposed algorithm could be well used for multiple linear feature detection in different applications.     

Top-hat by reconstruction operators based multi-scale multi-structuring element method for multiple linear feature detection with simple post-processing

To efficiently extract all the possible linear features in image, a multi-scale multi-structuring element top-hat by reconstruction operator based algorithm with simple post-processing is proposed in this paper. Multi-scale top-hat by reconstruction operator using multi-scale structuring elements is discussed, firstly. Also, through importing multi-structuring elements with linear shapes at different directions, multi-scale multi-structuring element top-hat by reconstruction operator for linear feature extraction is shown. By using the multi-scales of multi-structuring elements, the method of extracting all the possible linear feature regions in an image is proposed. After extracting the linear feature regions, the final detected linear features, which are expressed as lines with different shapes and lengths, are obtained through image binarisation and refinement. Experimental results on different types of images show that, the proposed algorithm is efficient for linear feature detection and could be widely used in different applications related to multiple linear feature detection.     

Image fusion through local feature extraction by using multi-scale top-hat by reconstruction operators

The purpose of image fusion is to combine useful image features of different original images into the final fusion image, which will produce one useful result image for different applications. One of the main difficulties of image fusion is extracting useful image features of different original images. In some cases, useful image features are local image features of the whole image. To efficiently extract local image features and produce an efficient fusion result, an image fusion algorithm based on the extracted local image features by using multi-scale top-hat by reconstruction operators is proposed in this paper. Firstly, multi-scale local feature extraction using multi-scale top-hat by reconstruction operators is discussed. Then, based on the extracted multi-scale local features of different original images, the useful image features for image fusion are constructed. Finally, the constructed useful image features for image fusion are combined into the final fusion image. Experimental results on different types of images show that, the proposed algorithm performs well for image fusion.     

Infrared dim small target enhancement using toggle contrast operator

A new infrared dim small target enhancement algorithm based on toggle contrast operator is proposed. Toggle contrast operator is modified and used to construct operators using the image features derived from dilation and erosion operators. Then, based on the constructed operators, the operators which could be used to estimate the clutter background of the original infrared dim small target image are proposed using the same strategy as the definition of opening. Finally, the infrared dim small target is well enhanced through subtracting the estimated background from the original image. Experimental results on infrared images with different types of targets verified that the proposed method could effectively enhance infrared dim small target, which would be very useful for infrared dim small target detection and tracking.     

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.     

一种新的全色图像与光谱图像融合方法研究

给出了一种新的综合伪彩色映射技术和小波变换理论的图像融合方法,并将其应用于全色图像和光谱图像的融合中.通过提取两个不同谱段光谱图像的共有信息和特别信息,并进行伪彩色映射融合,来增强目标与背景的对比度.同时将伪彩色映射融合后的图像进一步用IHS变换提取空间信息,在小波变换框架下将其与全色图像进行融合以提高目标的边缘细节信息,使所获得的融合结果不仅包含丰富的光谱信息的同时还具有较高的空间分辨率.仿真图和评价指标表明,该算法在增强目标与背景的对比度以及保留目标信息等方面具有较强的优势.     

Morphological impulsive noise removal based on multi scale dual hit-or-miss transforms

Impulsive noise removal is an active research area in optical signal processing. Morphological operators have been tried for impulsive noise removal. However, the performance is not very effective. Dual hit-or-miss transforms could identify the protruding bright or dark pixels like impulsive noise pixels in image, which may be used to construct effective algorithm for impulsive noise removal. In this paper, an algorithm based on the multi scale dual hit-or-miss transforms is demonstrated. Firstly, the positive impulsive noise pixels, which are bright pixels, are identified by multi scale hit-or-miss transform. Then, the negative impulsive noise pixels, which are dark pixels, are identified by multi scale dual hit-or-miss transform. Finally, the identified impulsive noise pixels are removed and replaced by a reasonable value estimated by adaptive median filter. Experimental results show that, because the multi scale dual hit-or-miss transforms could effectively and correctly identify the impulsive noise pixels, the noise pixels are correctly removed and the real image details could be well maintained.     

基于Retinex理论与概率非局部均值的红外图像增强方法

针对传统红外图像增强算法中细节模糊及过度增强的问题,提出了一种基于Retinex理论与概率非局部均值相结合的红外图像增强方法.首先通过单尺度Retinex方法调整图像中过暗与过亮部分的灰度级;然后利用概率非局部均值对图像进行分解处理得到基本层与细节层,对基本层采用直方图均衡化拉伸对比度,对细节层采用非线性函数进行增强;最后,将不同层次的结果融合得到对比度与细节增强的红外图像.用该方法对多组不同场景的红外图像进行仿真实验,并将其与多种增强方法进行主、客观对比分析,结果表明所提方法在红外图像的细节及对比度增强方面都获得了更好的效果.     

基于曲率滤波和梯度变换的图像增强

针对基于梯度变换的图像增强算法抗噪声干扰能力差的问题,引入曲率滤波理论,提出了基于高斯曲率滤波和梯度变换的图像增强算法.该算法通过对图像梯度场进行非线性变换来增强图像对比度,通过构造能量泛函,采用梯度下降法从变换后的梯度场重构出增强后的图像,并利用高斯曲率滤波对梯度下降法迭代过程中的重构图像及其各阶偏微分进行平滑,有效解决了图像重构过程中的噪声非线性放大和扩散问题,同时保留了丰富的细节信息.采用多组边缘模糊图像进行仿真实验,实验结果表明该算法在增强图像边缘对比度的同时,能够有效抑制噪声.     

结合小波域变换和空间域变换的图像增强方法

提出了一种结合了小波域和空间域处理方法的图像增强算法. 该算法首先对小波域中的高频系数进行修正,使图像具有更好的局部对比度和更丰富的细节,由于双树复小波变换（Dual-tree Complex Wavelet Transform,DT CWT）具有更好的方向选择性,在小波变换的过程中选用了这一方法;然后,通过空间域中的非线性变换,调整图像的整体对比度. 该算法可根据图像本身的特性实现参数的自动选择. 经过本文方法的处理所得的图像,无论在视觉效果上还是在统计上,都优于前人工作的结果.     

基于回形窗的弱小多目标图像分割方法

提出一种红外弱小多目标图像分割方法,用一个回形窗口和对比度阈值分割图像.对天空背景下低信噪比的红外弱小多目标图像序列能够有效的分割,抑制噪音干扰.将该方法与传统的图像分割方法做了比较,并对用不同阈值,不同窗口分割时的分割结果进行了分析.实验表明,该算法在执行效率和检测概率上能够取得满意的结果.     

基于波原子变换的红外复杂背景杂波抑制算法

针对红外图像弱小目标检测技术中复杂背景杂波干扰问题,提出了一种基于波原子变换的红外图像背景抑制算法。首先,采用波原子变换对图像进行多尺度和多方向分解,获得原始图像的多尺度和多方向细节特征;然后,根据目标和背景杂波信号的差异,通过频域变换设计的系数调整函数修正经波原子变换后各子带系数,再经波原子逆变换重构得到估计的背景图像;最后,将其与原始图像相减获得背景杂波抑制后的图像。用真实的红外图像序列进行实验,结果显示,与最大中值和小波变换两种算法相比,该算法能有效地抑制红外弱小目标复杂背景杂波,突出目标信号,提高信杂比,具有良好的背景抑制性能。     

A multi-focus image fusion algorithm based on an improved dual-channel PCNN in NSCT domain

This paper presents a multi-focus image fusion algorithm based on dual-channel PCNN in NSCT domain. The fusion algorithm based on multi-scale transform is likely to produce the pseudo-Gibbs effects and it is not effective to fuse the dim or partial bright images. To solve these problems, this algorithm will get a number of different frequency sub-image of the two images by using the NSCT transform, the selection principles of different subband coefficients obtained by the NSCT decomposition are discussed in detail, and the images are fused based on the improved dual-channel PCNN in order to determine the band-pass sub-band coefficient, at last fused image is obtained by using the inverse NSCT transform. Fusion rules based on dual-channel PCNN are used to solve the complexity of the PCNN parameter settings and long computing time problems. The experimental results show that the algorithm has overcome the defects of the traditional multi-focus image fusion algorithm and improved the fusion effect.     

基于Shearlet变换的自适应图像融合算法

针对多聚焦图像与多光谱和全色图像的成像特点,结合Shearlet变换具有较好的稀疏表示图像特征的性质,提出了一种新的图像融合规则.并基于此融合规则,提出了基于Shearlet变换的自适应图像融合算法.在多聚焦图像的融合算法中,分别对聚焦不同的图像进行Shearlet变换,并基于本文提出的融合规则,对分解后的高低频系数进行融合处理. 通过与多种算法的比较实验证明了本文提出的算法融合的图像具有更高的清晰度和更加丰富的细节信息.在多光谱和全色图像的融合处理中,提出了一种基于Shearlet变换与HSV变换相结合的图像融合方法.该算法首先对多光谱图像作HSV变换,将得到的V分量与全色图像进行Shearlet分解与融合,在融合过程中对分解系数选用特定的融合准则进行融合,最后将融合生成新的分量与H、S分量进行HSV逆变换产生新的RGB融合图像. 该算法在空间分辨率和光谱特性两方面达到了良好的平衡,融合后的图像在减少光谱失真的同时,有效增强了空间分辨率. 仿真实验证明,本文算法融合的图像与传统的多光谱和全色图像融合算法相比,具有更佳的融合性能和视觉效果.     

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。