Multi-spectral image fusion based on fractal features

Image fusion has been one of the main technical routines used to detect and recognize objects from images. Loss of information is unavoidable in fusion process. So it is very important contents of image fusion how to avoid the loss of useful information or how to preserve features which are helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been developed. Source images are firstly orthogonally decomposed according to wavelet transform theories, then fractal-based detection is held to each decomposed image. After the district containing man-made objects is detected, a special composing operator is adopted to reduce the information loss of man-made objects. This fractal-based algorithm is compared with a common weighted average fusion algorithm and the experimental results imply that the fractal-based multi-spectral fusion algorithm can effectively preserve the information of man-made objects with a high contrast.