A Perceptive-oriented Approach to Image Fusion

At present time image fusion is widely recognized as an important tool and has attracted a great deal of attention from the research community with the purpose of searching general formal solutions to a number of problems in different applications such as medical imaging, optical microscopy, remote sensing, computer vision and robotics. Image fusion consists of combining information from two or more images from the same sensor or from multiple sensors in order to improve the decision making process. Fused images from multiple sensors, often called multi-modal image fusion system, include at least, two image modalities ranging from visible to infrared spectrum and they provide several advantages over data images from a single sensor (Kor & Tiwary, 2004). An example of this can be found in medical imaging where it is common to merge functional activity as in single photon emission computed tomography (SPECT), positron emission tomography (PET) or magnetic resonance spectroscopy (MRS) with anatomical structures such as magnetic resonance image (MRI), computed tomography (CT) and ultrasound, which helps improve diagnostic performance and surgical planning (Guihong et al., 2001, Hajnal et al., 2001). An interesting example of single sensor fusion can be found in remote sensing, where pansharpening is an important task that combines panchromatic and multispectral optical data in order to obtain new multispectral bands that preserve their original spectral information with improved spatial resolution. Depending on the merging stage, common image fusion schemes can be classified into three categories: pixel, feature and decision levels (Pohl & van Genderen, 1998). Many fusion schemes usually employ pixel level fusion techniques but since features, that are sensitive to human visual system (HVS), are bigger than a pixel and they exist in different scales, it is necessary to apply multiresolution analysis which improves the reconstruction of relevant image features (Nava et al., 2008). Moreover, the image representation model used to build the fusion algorithm must be able to characterize perceptive-relevant image primitives. In the literature several methods of pixel level fusion have been reported using a transformation to perform data fusion, some of these transformations are: intensity-huesaturation transform (IHS), principal component analysis (PCA) (Qiu et al., 2005), the