2,776 research outputs found

    Improving fusion of surveillance images in sensor networks using independent component analysis

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    Survey on wavelet based image fusion techniques

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    Image fusion is the process of combining multiple images into a single image without distortion or loss of information. The techniques related to image fusion are broadly classified as spatial and transform domain methods. In which, the transform domain based wavelet fusion techniques are widely used in different domains like medical, space and military for the fusion of multimodality or multi-focus images. In this paper, an overview of different wavelet transform based methods and its applications for image fusion are discussed and analysed

    Revisiting Far/Near Infrared Pyramid-Based Fusion Types for Night Vision Using Matlab

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    The night vision imaging mechanisms are developed to increase the visibility beyond normal human perception capabilities. So far, night vision methods reported in literature, such as, Morphological, Low Pass Pyramid, Contrast Pyramid, Filter Subtract Decimate and Shift Invariant methods, the Laplacian fusion method has been rated, the best method [1][2]. In this research paper four different methods of fusion of images, Gradient, Wavelet, Quincuns Lifting, including Laplacian are processed using Matlab toolbox called Matifus for night vision. For comparing the results of processed images using above methods, Mean Opinion Score (MOS) is used. MOS result of Laplacian, wavelet, gradient and quincunx methods are compared. The MOS results on the scale of 1-5 indicate a score of 4.15 for Laplacian, that means the quality of image perceived by the scorers is rated between good and excellent. By using MOS and perceptually proving that Laplacian technique is better than all others for night vision systems. However Gradient scored 3.56, Wavelet scored 3.15 and lastly 2.22 was scored by Quincunx Lifting method

    Cognitive Image Fusion and Assessment

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    Multimodality and Multiresolution Image Fusion

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    Standard multiresolution image fusion of multimodal images may yield an output image with artifacts due to the occurrence of opposite contrast in the input images. Equal but opposite contrast leads to noisy patches, instable with respect to slight changes in the input images. Unequal and opposite contrast leads to uncertainty of how to interpret the modality of the result. In this paper a biased fusion is proposed to remedy this, where the bias is towards one image, the so-called iconic image, in a preferred spectrum. A nonlinear fusion rule is proposed to prevent that the fused image reverses the local contrasts as seen in the iconic image. The rule involves saliency and a local match measure. The method is demonstrated by artificial and real-life examples

    Comparative study of Image Fusion Methods: A Review

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    As the size and cost of sensors decrease, sensor networks are increasingly becoming an attractive method to collect information in a given area. However, one single sensor is not capable of providing all the required information,either because of their design or because of observational constraints. One possible solution to get all the required information about a particular scene or subject is data fusion.. A small number of metrics proposed so far provide only a rough, numerical estimate of fusion performance with limited understanding of the relative merits of different fusion schemes. This paper proposes a method for comprehensive, objective, image fusion performance characterization using a fusion evaluation framework based on gradient information representation. We give the framework of the overallnbsp system and explain its USAge method. The system has many functions: image denoising, image enhancement, image registration, image segmentation, image fusion, and fusion evaluation. This paper presents a literature review on some of the image fusion techniques for image fusion like, Laplace transform, Discrete Wavelet transform based fusion, Principal component analysis (PCA) based fusion etc. Comparison of all the techniques can be the better approach fornbsp future research

    Iterative Multiscale Fusion and Night Vision Colorization of Multispectral Images

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