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Comprehensive and Comparative Study of Image Fusion Techniques

Keywords: Discrete Wavelet Transform , Image Fusion , Pyramid Methods , Principal Component Analysis

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Image Fusion is one of the major research fields in image processing. Image Fusion is a process of combining the relevant information from a set of images, into a single image, wherein the resultant fused image will be more informative and complete than any of the input images. Image fusion process can be defined as the integration of information from a number of registered images without the introduction of distortion. It is often not possible to get an image that contains all relevant objects in focus. One way to overcome this problem is image fusion, in which one can acquire a series of pictures with different focus settings and fuse them to produce an image with extended depth of field. Image fusion techniques can improve the quality and increase the application of these data. This paper discusses the three categories of image fusion algorithms – the basic fusion algorithms, the pyramid based algorithms and the basic DWT algorithms. It gives a literature review on some of the existing image fusion techniques for image fusion like, primitive fusion (Averaging Method, Select Maximum, and Select Minimum), Discrete Wavelet transform based fusion, Principal component analysis (PCA) based fusion etc. The purpose of the paper is to elaborate wide range of algorithms their comparative study together. There are many techniques proposed by different authors in order to fuse the images and produce the clear visual of the image. Hierarchical multiscale and multiresolution image processing techniques, pyramid decomposition are the basis for the majority of image fusion algorithms. All these available techniques are designed for particular kind of images. Until now, of highest relevance for remote sensing data processing and analysis have been techniques for pixel level image fusion for which many different methods have been developed and a rich theory exists. Researchers have shown that fusion techniques that operate on such features in the transform domain yield subjectively better fused images than pixel based techniques. For this purpose, feature based fusion techniques that are usually based on empirical or heuristic rules are employed. Because a general theory is lacking fusion, algorithms are usually developed for certain applications and datasets. To implement the pixel level fusion, arithmetic operations are widely used in time domain and frequency transformations are used in frequency domain. In many applications area of navigation guidance, object detection and recognition, medical diagnosis, satellite imaging for remote sensing, rob vision, military a


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