Improving Multimodality Image Fusion through Integrate AFL and Wavelet Transform

Background

Multimodalities data fusion has become a discipline to which more and more general formal solutions to a number of application cases are required. Several situations in image processing simultaneously require high spatial and high spectral information in a single composite image. This is important in medical diagnosis, remote sensing. However, the multimodalities are not capable of providing such information either by design or because of observational constraints. One possible solution for this is data fusion. Image fusion is the process of combining information from two or more multimodalities images of a scene into a single composite image that is more informative and is more suitable for visual perception or computer processing. The aim of multimodalities image fusion is to integrate complementary information of multi-sensor, multi-temporal and/or multitier into one new image. The goal is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information particular to an specific application or task. An area based maximum selection rule, and consistency verification steps are used for feature selection. The algorithms are checked for multi sensor as well as multi focus image fusion.