期刊名称：International Journal of Engineering and Computer Science
印刷版ISSN：2319-7242
出版年度：2016
卷号：5
期号：2
页码：15661-15667
DOI：10.18535/ijecs/v5i2.2
出版社：IJECS
摘要：Image fusion is to reduce uncertainty and minimize redundancy. It is a process of combining the relevant information from a set ofimages, into a single image, wherein the resultant fused image will be more informative and complete than any of the inputimages. Till date the image fusion techniques were like DWT or pixel based. These conventional techniques were not thatefficient and they did not produced the expected results as the edge preservance, spatial resolution and the shift invariance are thefactors that could not be avoided during image fusion. This paper discusses the implementation of two categories of image fusion.The Stationary wavelet transform (SWT), and Principal component analysis (PCA). The Stationary wavelet transform (SWT) isa wavelet transform algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transform (DWT).Whereas The Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a setof observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.To overcome the disadvantages of the earlier techniques used for image fusion a new hybrid technique is proposed that works bycombining the SWT and PCA i.e. stationery wavelet transform and principal component analysis. This hybrid technique isproposed to obtain a better efficient and a better quality fused image which will have preserved edges and its spatial resolution andshift invariance will be improved. This hybrid technique will produce better fusion results. The image obtained after fusion usingproposed technique will be of better quality than the images fused using conventional techniques
