A Review of Image Fusion Methods


Authors : Nidhi Taxak, Sachin Singhal.

Volume/Issue : Volume 3 - 2018, Issue 8 - August

Google Scholar : https://goo.gl/DF9R4u

Scribd : http://tiny.cc/onqd5y

Thomson Reuters ResearcherID : https://goo.gl/KTXLC3

Image fusion is defined as the process of combining multiple images from similar background to form a whole single image. This combined image will provide a lot of information that cannot be found when they are available in separate images. This process is used mainly for obtaining certain major information that are hidden in a single image and they are also used for the noise available in the image. There are a number of varieties available that produces noise in an image. Some of the common noise include Gaussian, impulse, and various other noises available in the environment. Due to the problem of this process sometimes the quality of the images are being affected. So in order to find the problems existing in a image the fusion or combination technique is been used and they can be e done using three methods like pixel level method, feature level and decision level methods of combination. The basic technology that is being used for combining these multiple image is major special technique or temporal technique. Some of the most common special technique that are being used in spatial domains are average method, PCA Fusion method and high pass filtering method. Some of the temporal methods include direct cosine transform, discrete wavelet transformation and temporal domain fusion methods. Both of these methods have their own set of positives and negatives. One of the most common problem that occurs in this method is the colour artefact problem and hence this paper mainly focuses on the problems that occur in various fusion techniques and how to solve them.

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