Improving Independent Component Analysis Using Support Vector Machines for Multimodal Image Fusion

The objective of this study is to combine multiple images of a scene acquired by different sensors to create a new image with all important information from the input images. Recent studies show that bases trained using Independent Component Analysis (ICA) is effective in multisensor fusion and has improved performance over traditional wavelet approaches. In the ICA based fusion, the coefficients of the input images are combined simply by selecting the coefficients with maximum magnitude. But this method resulted in fused images with poor contrast, due to the distortion introduced in constant background areas. The performance of ICA based fusion can be greatly improved by using a region based approach with intelligent decision making in order to choose the significant regions in the source images. Hence, a new region based image fusion algorithm for combining visible and Infrared (IR) images using Independent Component Analysis and Support Vector Machines (SVM) is proposed. Region based joint segmentation of the source images is carried out in the spatial domain and important features of each region are computed in spatial and transform domain. A Support Vector Machine is trained to select the regions from the source images with significant features and the corresponding ICA coefficients are combined to form the fused ICA representation. The proposed algorithm is applied to different sets of multimodal images to validate the robustness of the algorithm and compared with some standard image fusion methods. The fusion results demonstrate that the proposed scheme performs better than the state-of-the-art image fusion methods and show a significant improvement in Entropy, Petrovic and Piella evaluation metrics.