基于像素值选择的红外与可见光图像融合方法

在夜视系统中,红外图像可以提供可将光图像没有的信息,但是红外图像又缺少许多可视化的重要特征.图像融合技术能够融合具有相关信息的红外图像和可将光图像.本文中提出了一种基于小波变换的图像融合技术,并且运用此技术做了相关的图像融合试验.

多幅相同场景的图像的降噪

随着数字图像采集设备的发展,利用多幅相同场景的图像以获得更高质量的图像的方法已具有现实意义。文章分析了多幅图像降噪中面临的问题,并以高斯白噪声污染图像为例,论证了多幅图像的最佳线性组合方法,采用自适应维纳滤波与均值滤波相结合的降噪方法,试验证明了多幅图像降噪效果比单幅图像降噪效果明显要好。

Infrared polarization image fusion based on combination of NSST and improved PCA

In view of the problem that current mainstream fusion method of infrared polarization image—Multiscale Geometry Analysis method only focuses on a certain characteristic to image representation.And spatial domain fusion method,Principal Component Analysis(PCA)method has the shortcoming of losing small target,this paper presents a new fusion method of infrared polarization images based on combination of Nonsubsampled Shearlet Transformation(NSST)and improved PCA.This method can make full use of the effectiveness to image details expressed by NSST and the characteristics that PCA can highlight the main features of images.The combination of the two methods can integrate the complementary features of themselves to retain features of targets and image details fully.Firstly,intensity and polarization images are decomposed into low frequency and high frequency components with different directions by NSST.Secondly,the low frequency components are fused with improved PCA,while the high frequency components are fused by joint decision making rule with local energy and local variance.Finally,the fused image is reconstructed with the inverse NSST to obtain the final fused image of infrared polarization.The experiment results show that the method proposed has higher advantages than other methods in terms of detail preservation and visual effect.

Automated Registration for Infrared Image Based on Wavelet Analysis

To develop a quick, accurate and antinoise automated image registration technique for infrared images, the wavelet analysis technique was used to extract the feature points in two images followed by the compensation for input image with angle difference between them. A hi erarchical feature matching algorithm was adopted to get the final transform parameters between the two images. The simulation results for two infrared images show that the method can effectively, quickly and accurately register images and be antinoise to some extent.

Sinogram fusion-based metal artifact correction method

To solve the problem that metal artifacts severely damage the clarity of the organization structure in computed tomography（CT） images, a sinogram fusion-based metal artifact correction method is proposed. First, the metal image is segmented from the original CT image by the pre-set threshold. The original CT image and metal image are forward projected into the original projection sinogram and metal projection sinogram, respectively. The interpolation-based correction method and mean filter are used to correct the original CT image and preserve the edge of the corrected CT image, respectively. The filtered CT image is forward projected into the filtered image sinogram. According to the position of the metal sinogram in the original sinogram and filtered image sinogram, the corresponding sinograms PM^D （ in the original sinogram） and PM^C （ in the filtered image sinogram）can be acquired from the original sinogram and filtered image sinogram, respectively. Then, PM^D and PM^C are fused into the fused metal sinogram PM^F according to a certain proportion.The final sinogram can be acquired by fusing PM^F , PM^D and the original sinogram P^O. Finally, the final sinogram is reconstructed into the corrected CT image and metal information is compensated into the corrected CT image.Experiments on clinical images demonstrate that the proposed method can effectively reduce metal artifacts. A comparison with classical metal artifacts correction methods shows that the proposed metal artifacts correction method performs better in metal artifacts suppression and tissue feature preservation.

Application of Image Fusion Methods to Cell Imaging Processing

Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.

Fluorescence molecular imaging system and fusion algorithm based on 2CCD camera

Infrared and visible light images can be obtained simultaneously by building fluorescence imaging system,which includes fluorescence excitation,images acquisition,mechanical part,image transmission and processing section.This system studied the 2charge-coupled device(CCD)camera(AD-080CL)of the JAI company.Fusion algorithm of visible light and near infrared images was designed for the fluorescence imaging system with wavelet transform image fusion algorithm.In order to enhance the fluorescent moiety of the fusion image,the luminance value of the green component of the color image was changed.And using microsoft foundation classes(MFC)application architecture,the supporting software system was bulit in VS2010 environment.

Depression discrimination using fMRI and DTI data by wavelet based fusion scheme

Both functional magnetic resonance imaging （fMRI） and diffusion tensor imaging （DTI） can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis （PCA） is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.

MULTI-SOURCE REMOTE SENSING IMAGE FUSION BASED ON SUPPORT VECTOR MACHINE

Remote Sensing image fusion is an effective way to use the large volume ofdata from multi-source images. This paper introduces a new method of remote sensing image fusionbased on support vector machine (SVM), using high spatial resolution data SPIN-2 and multi-spectralremote sensing data SPOT-4. Firstly, the new method is established by building a model of remotesensing image fusion based on SVM. Then by using SPIN-2 data and SPOT-4 data, image classificationfusion is tested. Finally, an evaluation of the fusion result is made in two ways. 1) Fromsubjectivity assessment, the spatial resolution of the fused image is improved compared to theSPOT-4. And it is clearly that the texture of the fused image is distinctive. 2) From quantitativeanalysis, the effect of classification fusion is better. As a whole, the re-suit shows that theaccuracy of image fusion based on SVM is high and the SVM algorithm can be recommended forapplication in remote sensing image fusion processes.

CT image fusion in the evaluation of radiation treatment planning for non-small cell lung cancer

Objective: We studied the application of CT image fusion in the evaluation of radiation treatment planning for non-small cell lung cancer (NSCLC). Methods: Eleven patients with NSCLC, who were treated with three-dimensional con-formal radiation therapy, were studied. Each patient underwent twice sequential planning CT scan, i.e., at pre-treatment, and at mid-treatment for field reduction planning. Three treatment plans were established in each patient: treatment plan A was based on the pre-treatment planning CT scans for the first course of treatment, plan B on the mid-treatment planning CT scans for the second course of treatment, and treatment plan F on the fused images for the whole treatment. The irradiation doses received by organs at risk in the whole treatment with treatment A and B plans were estimated by the plus of the parameters in treatment plan A and B, assuming that the parameters involve the different tissues (i.e. V20=AV20+BV20), or the same tissues within an organ (i.e. Dmax=ADmax+BDmax). The assessment parameters in the treatment plan F were calculated on the basis of the DVH of the whole treatment. Then the above assessment results were compared. Results: There were marked differ-ences between the assessment results derived from the plus of assessment parameters in treatment plan A and B, and the ones derived from treatment plan F. Conclusion: When a treatment plan is altered during the course of radiation treatment, image fusion technique should be performed in the establishment of a new one. The estimation of the assessment parameters for the whole treatment with treatment plan A and B by simple plus, is inaccurate.

Multi-sensors Image Fusion via NSCT and GoogLeNet

In order to improve the detail preservation and target information integrity of different sensor fusion images,an image fusion method of different sensors based on non-subsampling contourlet transform(NSCT)and GoogLeNet neural network model is proposed. First,the different sensors images,i. e.,infrared and visible images,are transformed by NSCT to obtain a low frequency sub-band and a series of high frequency sub-bands respectively.Then,the high frequency sub-bands are fused with the max regional energy selection strategy,the low frequency subbands are input into GoogLeNet neural network model to extract feature maps,and the fusion weight matrices are adaptively calculated from the feature maps. Next,the fused low frequency sub-band is obtained with weighted summation. Finally,the fused image is obtained by inverse NSCT. The experimental results demonstrate that the proposed method improves the image visual effect and achieves better performance in both edge retention and mutual information.

Application of Preoperative CT/MRI Image Fusion in Target Positioning for Deep Brain Stimulation

Objective To explore the efficacy of target positioning by preoperative CT/MRI image fusion technique in deep brain stimulation.Methods We retrospectively analyzed the clinical data and images of 79 cases(68 with Parkinson's disease,11 with dystonia) who received preoperative CT/MRI image fusion in target positioning of subthalamic nucleus in deep brain stimulation.Deviation of implanted electrodes from the target nucleus of each patient were measured.Neurological evaluations of each patient before and after the treatment were performed and compared.Complications of the positioning and treatment were recorded.Results The mean deviations of the electrodes implanted on X,Y,and Z axis were 0.5 mm,0.6 mm,and 0.6 mm,respectively.Postoperative neurologic evaluations scores of unified Parkinson's disease rating scale(UPDRS) for Parkinson's disease and Burke-Fahn-Marsden Dystonia Rating Scale(BFMDRS) for dystonia patients improved significantly compared to the preoperative scores(P<0.001); Complications occurred in 10.1%(8/79) patients,and main side effects were dysarthria and diplopia.Conclusion Target positioning by preoperative CT/MRI image fusion technique in deep brain stimulation has high accuracy and good clinical outcomes.

Multiresolution image fusion scheme based on fuzzy region feature

This paper proposes a novel region based image fusion scheme based on multiresolution analysis. The low frequency band of the image multiresolution representation is segmented into important regions, sub-important regions and background regions. Each feature of the regions is used to determine the region’s degree of membership in the multiresolution representation, and then to achieve multiresolution representation of the fusion result. The final image fusion result can be obtained by using the inverse multiresolution transform. Experiments showed that the proposed image fusion method can have better performance than existing image fusion methods.

Synthetically Evaluation System for Multi-source Image Fusion and Experimental Analysis

Study on the evaluation system for multi-source image fusion is an important and necessary part of image fusion. Qualitative evaluation indexes and quantitative evaluation indexes were studied. A series of new concepts, such as independent single evaluation index, union single evaluation index, synthetic evaluation index were proposed. Based on these concepts, synthetic evaluation system for digital image fusion was formed. The experiments with the wavelet fusion method, which was applied to fuse the multi-spectral image and panchromatic remote sensing image, the IR image and visible image, the CT and MRI image, and the multi-focus images show that it is an objective, uniform and effective quantitative method for image fusion evaluation.

Study of the impact of CT/CT image fusion radiotherapy on V_(20) and radiation pneumonitis of non-small cell lung cancer

Objective:The aim of our study was to investigate the value of CT/CT image fusion radiation treatment planning in non-small cell lung cancer(NSCLC) and the impact on V20 and radiation pneumonitis(RP).Methods:Patients who were pathologically or cytologically diagnosed of stage IIIA and IIIB NSCLC were treated with three-dimensional conformal radiation therapy(4000 cGy).Forty patients got at least 25% tumor reduction were randomly divided into two groups:group A of regular shrink field radiotherapy(20 cases) and group B of CT/CT image fused shrink field radiotherapy(20 cases).Dosage reached 6600 cGy.Clinical data,V20 and RP were observed within 3 months after radiotherapy.Statistical analysis was conducted for the NSCLC patients.Results:22.5%(9/40) patients got RP during follow-up.Group A accounted for 6 cases(30%),and group B had 3 cases(15%).There was no marked difference between the two groups(P = 0.256),univariate analysis revealed that the IV20 of A and B groups,and IV20 and CV20 of all patients were statistically related to the incidence of RP(P < 0.05).With Wilcoxon method assay,the ipsilateral lung V20 and contralateral lung V20 had statistical significance between the two groups(P < 0.05).Conclusion:The CT/CT image infusion treatment planning could increase the radical dosage with better tumor control probability but won't increase adverse reaction.

Investigation of Image Fusion Between High-Resolution Image and Multi-spectral Image

On the basis of a thorough understanding of the physical characteristics of remote sensing image, this paper employs the theories of wavelet transform and signal sampling to develop a new image fusion algorithm. The algorithm has been successfully applied to the image fusion of SPOT PAN and TM of Guangdong province, China. The experimental results show that a perfect image fusion can be built up by using the image analytical solution and re-construction in the image frequency domain based on the physical characteristics of the image formation. The method has demonstrated that the results of the image fusion do not change spectral characteristics of the original image.

Fusion and Classification of Beijing-1 Small Satellite Remote Sensing Image for Land Cover Monitoring in Mining Area

In order to promote the application of Beijing-1 small satellite(BJ-1) remote sensing data,the multispectral and panchromatic images captured by BJ-1 were used for land cover classification in Pangzhuang Coal Mining.An improved Intensity-Hue-Saturation(IHS) fusion algorithm is proposed to fuse panchromatic and multispectral images,in which intensity component and panchromatic image are combined using the weights determined by edge pixels in the panchromatic image identified by grey absolute correlation degree.This improved IHS fusion algorithm outper-forms traditional IHS fusion method to a certain extent,evidenced by its ability in preserving spectral information and enhancing spatial details.Dempster-Shafer(D-S) evidence theory was adopted to combine the outputs of three member classifiers to generate the final classification map with higher accuracy than that by any individual classifier.Based on this study,we conclude that Beijing-1 small satellite remote sensing images are useful to monitor and analyze land cover change and ecological environment degradation in mining areas,and the proposed fusion algorithms at data and decision levels can integrate the advantages of multi-resolution images and multiple classifiers,improve the overall accuracy and produce a more reliable land cover map.

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine

The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems. Based on image multi-feature fusion and support vector machine, a new method to identify flow regime in two-phase flow was presented. Firstly, gas-liquid two-phase flow images including bub- bly flow, plug flow, slug flow, stratified flow, wavy flow, annular flow and mist flow were captured by digital high speed video systems in the horizontal tube. The image moment invariants and gray level co-occurrence matrix texture features were extracted using image processing techniques. To improve the performance of a multiple classifier system, the rough sets theory was used for reducing the inessential factors. Furthermore, the support vector machine was trained by using these eigenvectors to reduce the dimension as flow regime samples, and the flow regime intelligent identification was realized. The test results showed that image features which were reduced with the rough sets theory could excellently reflect the difference between seven typical flow regimes, and successful training the support vector machine could quickly and accurately identify seven typical flow regimes of gas-liquid two-phase flow in the horizontal tube. Image multi-feature fusion method provided a new way to identify the gas-liquid two-phase flow, and achieved higher identification ability than that of single characteristic. The overall identification accuracy was 100%, and an estimate of the image processing time was 8 ms for online flow regime identification.

COMBINING SCENE MODEL AND FUSION FOR NIGHT VIDEO ENHANCEMENT

This paper presents a video context enhancement method for night surveillance. The basic idea is to extract and fuse the meaningful information of video sequence captured from a fixed camera under different illuminations. A unique characteristic of the algorithm is to separate the image context into two classes and estimate them in different ways. One class contains basic surrounding scene in- formation and scene model, which is obtained via background modeling and object tracking in daytime video sequence. The other class is extracted from nighttime video, including frequently moving region, high illumination region and high gradient region. The scene model and pixel-wise difference method are used to segment the three regions. A shift-invariant discrete wavelet based image fusion technique is used to integral all those context information in the final result. Experiment results demonstrate that the proposed approach can provide much more details and meaningful information for nighttime video.

Image Fusion Based on Complex Contourlet Transform and Nonnegative Matrix Factorization

An image fusion method combining complex contourlet transform(CCT) with nonnegative matrix factorization(NMF) is proposed in this paper.After two images are decomposed by CCT,NMF is applied to their highand low-frequency components,respectively,and finally an image is synthesized.Subjective-visual-quality of the image fusion result is compared with those of the image fusion methods based on NMF and the combination of wavelet /contourlet /nonsubsampled contourlet with NMF.The experimental results are evaluated quantitatively,and the running time is also contrasted.It is shown that the proposed image fusion method can gain larger information entropy,standard deviation and mean gradient,which means that it can better integrate featured information from all source images,avoid background noise and promote space clearness in the fusion image effectively.