21-07-2012, 01:23 PM
IMAGE FUSION USING DISCRETE WAVELET TRANSFORMATION
image fusion1.doc (Size: 2 MB / Downloads: 48)
ABSTRACT:
Image fusion is the process of combining images taken from different sources, to obtain better situational awareness. In fusing source images, the objective is to combine the most relevant information from source images into a composite image. There are many Image Fusion techniques based on signal, pixel, feature and symbol level fusion.A widely used approach to image fusion involves finding the discrete transform which does the work of fusion for the given images. In this paper we present the discrete types of wavelet and cosine transforms and also the sub groups of wavelets that are applicable to a wide class of image fusion problems. Experimental results demonstrate the improved quality of the images.
1.) Discrete wavelet transformation (DWT):-
This is a transformation which can reduce the computation up to certain level. This transformation selects few scales and positions based on the powers of the two-Dyadic Scales and positions; hence this analysis becomes very efficient and accurate. Such an analysis is obtained from discrete wavelet transformation (DWT).
2.) Discrete cosine transformation (DCT):-
This is a transformation similar to the Discrete Fourier transformation (DFT), but the main difference in using DCT is that it uses only cosine waves whereas Discrete Fourier transformation (DFT) uses sine and cosine waves to represent a signal the significance of using DCT is that it is purely real unlike DFT which is complex.
Basically this paper makes the use of the DWT.
THEORY:
Image Fusion techniques use different fusion techniques to combine multiple images into a single fused image.
Image Fusion produces a single image by combining information from a set of source images together, using pixel, and feature or decision level techniques. The fused image contains greater information content for the scene than any one of the individual image sources alone. The reliability and overall detail of the image is increased, because of the addition of analogous and complementary information. Image fusion requires that images be registered first before they are fused.
CONCLUSIONS:
According to the output observed it can be concluded that in the image fusion algorithm the blur or the poor quality region
in the image is being converted into the sharp or the good quality region and resultant one can obtained a clear , Better quality and a sharp image without effecting the picture details in the image.