- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Enhancement of dim targets in a sea background based on long-wave infrared polarisation features
摘要： According to Fresnel's formula and the energy conservation law, a model combining the infrared reflected effect and emitted effect is developed to calculate the polarisation degree. With this model, the polarisation degree difference between the sea surface and ship target in long-wave infrared is simulated. To solve the problem of dim targets detection in a sea background, based on the polarisation difference of the sea surface and ship targets, a method of the non-subsampled shearlets transformation is proposed to fuse the intensity image and polarisation image. The algorithm of distribution coefficients is applied to improve the contrast ratio between targets to background in low-frequency subbands. The denoise scheme of the adaptive threshold is adopted to suppress noise and the conceptions of local direction contrast and region gradient are used as a choosing scheme to the preserve features and edges of images in high-frequency subbands. Image evaluation indices of target contrast with the background and local signal-to-noise ratio are used to evaluate the enhancement effect of fused images. Results show that the evaluation indices of fused images with polarisation features are significantly improved, and comparisons with existing methods demonstrate the effectiveness and reliability of the proposed method.
关键词： infrared polarisation,image fusion,sea background,dim targets,non-subsampled shearlets transformation
更新于2025-09-23 15:21:21
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[IEEE 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) - Hangzhou, China (2018.8.25-2018.8.26)] 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) - Fusion of Infrared and Visible Images through a Hybrid Image Decomposition and Sparse Representation
摘要： Aiming at the fusion of the infrared and visible images, a novel image fusion framework based on hybrid image decomposition and sparse representation is proposed in this paper. Firstly, the Gaussian and guided filters are used to decompose the source images into the small-scale texture details, large-scale edge and coarse-scale image information. The main infrared features are maintained in the large-scale edge information, which are used to determine the fused weights sparse representation based fusion method is adopted for the fusion of the edge texture details and information, which makes the final fused image can effectively highlight the infrared targets, while preserving the texture details of the visible images as much as possible. So, the fused image is more consistent with the human visual perception effect. Experimental results show that method is superior to the currently used popular image fusion methods.
关键词： image fusion,guided filtering,hybrid image decomposition,sparse representation
更新于2025-09-23 15:21:01
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Colour Image Represention of Multispectral Image Fusion
摘要： The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal- infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.
关键词： Grayscale image,cholesky decomposition,Multispectral image fusion,principal component analysis
更新于2025-09-23 15:21:01
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[IEEE 2019 28th Wireless and Optical Communications Conference (WOCC) - Beijing, China (2019.5.9-2019.5.10)] 2019 28th Wireless and Optical Communications Conference (WOCC) - Fast Bi-dimensional Empirical Mode based Multisource Image Fusion Decomposition
摘要： Bi-dimensional empirical mode decomposition can decompose the source image into several Bi-dimensional Intrinsic Mode Functions. image decomposition, interpolation is needed and the upper and lower envelopes will be drawn. However, these interpolations and the drawings of upper and lower envelopes require a lot of computation time and manual screening. This paper proposes a simple but effective method that can maintain the characteristics of the original BEMD method, and the Hermite interpolation reconstruction method is used to replace the surface interpolation, and the variable neighborhood window method is used to replace the fixed neighborhood window method. We call it fast bi-dimensional empirical mode decomposition of the variable neighborhood window method based on research characteristics, and we finally complete the image fusion. The empirical analysis shows that this method can overcome the shortcomings that the source image features and details information of BIMF component decomposed from the original BEMD method are not rich enough, and reduce the calculation time, and the fusion quality is better.
关键词： fast bi-dimensional empirical mode decomposition,image fusion,Hermite interpolation,variable neighborhood window method
更新于2025-09-23 15:21:01
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The intelligent vehicle target recognition algorithm based on target infrared features combined with lidar
摘要： The intelligent vehicle target detection system can sense and recognize the surrounding pedestrians, vehicles and other objects through sensors, which is the basis for achieving intelligent vehicle unmanned driving. The laser imaging radar actively emits laser light and receives its reflected echo, which can form an angle-angle-distance-intensity image, making it easier to realize target recognition. The combination of the lidar and the infrared characteristics of the target can obtain more information and improve target recognition and anti-interference ability. In order to achieve fast and accurate moving target detection in a complex battlefield environment, this paper studies lidar imaging and target infrared features, as well as intelligent vehicle target detection, and proposes a target recognition method that combines target infrared features and lidar. Compensation makes it difficult to describe the disadvantages of moving targets in a single source data. The experimental results show that the laser and infrared fusion detection algorithm does not increase the complexity of the algorithm, which greatly improves the adaptability and robustness of the vehicle target detection algorithm, and improves the accuracy of the measurement detection algorithm.
关键词： Target recognition,Image fusion,Infrared feature,Infrared radar
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sentinel-1 and Sentinel-2 Data Fusion for Urban Change Detection
摘要： In this paper a new approach based on the fusion of Sentinel-1 and Sentinel-2 products to map urban change detection and to observe suburb’s development is presented. The algorithm developed can process data in a fast, automatic and accurate way. To reach this goal, the processing chain uses an iterative multitemporal approach based, for each iteration, on three procedures. The first and second ones are based on Pulse Coupled Neural Network (PCNN) applied to SAR and optical images, respectively, while the third processing is an optical multiband filter, implementing the spectral difference computation. The three outputs of each iteration are fused together by means of a weighted average formulation. The algorithm may deal with multitemporal acquisitions to improve the overall accuracy in the detection of urban changes by the integration of the outputs at different time intervals.
关键词： Sentinel-2,image fusion,global monitoring urbanization,Sentinel-1,change detection
更新于2025-09-23 15:21:01
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Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model
摘要： In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets.
关键词： image fusion,different resolutions,total variation,infrared
更新于2025-09-23 15:21:01
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Biorthogonal Halfband Perfect Reconstruction Filterbank for Multimodal Image Fusion
摘要： To design a wavelet filter bank which helps to detect more prominently continuous changes in tumor cells. In this paper, a method of designing two-channel wavelet base FIR filter bank using factorization of a half band filter is presented. Here factorization is done considering maximum vanishing moments for construction of decomposition as well as reconstruction filters. The 14 order maximally flat halfband filter is proposed with factorization, leading to design of 8/8 orthogonal and 9/7 & 6/10 symmetric filters. The fusion performance of designed wavelet filter bank is evaluated using various performance metrics, like, cross entropy, standard deviation, mean square error and PSNR. The results are compared with the Daubechies filter bank where db4 is used for implementation. It is clear from the results that designed filter bank improves fusion performance. Further, proposed filters have maximum number of vanishing moments which gives smooth scaling and wavelet functions and consequently provides flat frequency response. The designed 8/8 orthogonal wavelet filters are implemented in fusion application for multimodal biomedical images of a subject for detection of an abnormality (cancerous growth). The novelty of this method is the adaptive design of the filterbank for the given multimodal images, so that fused images shall have more clarity, further, it will help to improve the results with enhancement in the entropy levels of fused image.
关键词： Spectral Factorization,Medical Image Processing,Filter Banks,Multimodal Image Fusion,Wavelet Transforms,Half Band Filter
更新于2025-09-19 17:15:36
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Multi-scale fidelity measure for image fusion quality assessment
摘要： Image fusion is considered an effective enhancing methodology widely included in high-quality imaging systems. Nevertheless, like other enhancing techniques, output quality assessment is made within small sample subjective evaluation studies which are very limited in predicting the human-perceived quality of general image fusion outputs. Simple, blind, universal and perceptual-like methods for assessing composite image quality are still a challenge, partially solved only in particular applications. In this paper, we propose a fidelity measure, called MS-QW with two major characteristics related to natural image statistics framework: a multi-scale computation and a structural similarity score. In our experiments, we correlate the scores of our measure with subjective ratings and state of art measures included in the 2015 Waterloo IVC multi-exposure fusion (MEF) image database. We also use the measure to rank correctly the classical general fusion methods included in the Image Fusion Toolbox for medical, infra-red and multi-focus image examples. Moreover, we study the scores variability and statistical discrimination power with the TNO night vision database using the Friedman test. Finally, we define a new leave one out procedure based on our fidelity measure that selects the best subset of images (within a collection of distorted and unregistered cellphone type images) that provides a defect-free composite output. We exemplify the procedure with the fusion of a collection of images from Latour and Van Dongen paintings suffering from glass highlights and speckle noise, among other artifacts. The proposed multiscale quality measure MS-QW demonstrates improvement over the previous single-scale similarity measures towards a fidelity assessment between quantitative image fusion quality metrics and human perceptual qualitative scores.
关键词： structural similarity,statistical performance assessment,high quality photographs of paintings,multi-scale measures,image fusion quality assessment
更新于2025-09-19 17:15:36
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Image Fusion Based on Kernel Estimation and Data Envelopment Analysis
摘要： This paper reports the improvement of the image quality during the fusion of remote sensing images by minimizing a novel energy function. First, by introducing a gradient constraint term in the energy function, the spatial information of the panchromatic image is transferred to the fused results. Second, the spectral information of the multispectral image is preserved by importing a kernel function to the data fitting term in the energy function. Finally, an objective parameter selection method based on data envelopment analysis (DEA) is proposed to integrate state-of-the-art image quality metrics. Visual perception measurement and selected fusion metrics are employed to evaluate the fusion performance. Experimental results show that the proposed method outperforms other established image fusion techniques.
关键词： Data envelopment analysis,mutual information.,image fusion
更新于2025-09-19 17:15:36