Pixel-Level Image Fusion Scheme based on Linear Algebra

A Perceptive-Oriented Approach to Image Fusion

“…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 discrete wavelet transform (DWT) (Aguilar et al, 2007, Chipman et al, 1995, Li et al, 1994, dual-tree complex wavelet transform (DTCWT) (Kingsbury, 2001, Hill & Canagarajah, 2002, the contourlet transform (CW) (Yang et al, 2007), the curvelet transform (CUW) (Mahyari & Yazdi, 2009), and the Hermite transform (HT) (Escalante-Ramírez & López-Caloca, 2006, Escalante-Ramírez, 2008. In essence, all these transformations can discriminate between salient information and constant or non-textured background.…”

Image Fusion Based on Lifting Wavelet Transform

“…Image fusion is a powerful technique for image analysis and computer vision that can reduce errors in detection and recognition of objects by incorporating complementary information from several sources. The resulting fused image improves computer analysis tasks such as segmentation, feature extraction and object recognition [1] .…”

“…However, when applied to practical image fusion, it is found that traditional wavelet transform has some limitations: (1) with traditional wavelet transform, some floating numbers are created, which makes reconstruction of precise original signals be difficult because of limited word length of a computer. (2) It cannot transform images with arbitrary size.…”