Pixel- and Region-Based Image Fusion Using the Parameterized Logarithmic Stationary Wavelet Transform

摘要

Image fusion is the effective combination of multiple images into a single fused image. The goal of the fusion is use the similar and complementary information of the source images in order to obtain an informative depiction of the scene for further processing. Many multi-scale image fusion algorithms have been formulated on the basis that the human visual system is sensitive to edge information. However, these algorithms make use of standard mathematical operators, which do not reflect human visual system characteristics over a large range of background luminance intensities. Accordingly, this paper proposes new image fusion algorithms using a new Parameterized Logarithmic Stationary Wavelet Transform (PL-SWT), which combines the advantages of the Stationary Wavelet Transform (SWT) and the Parameterized Logarithmic Image Processing (PLIP) model, a parameterized framework for processing images. An analysis of the PLIP model shows that it is capable of providing a balance between logarithmic and standard mathematical operators based on image dependent characteristics. Consequently, the use of the parameterized model is extended for to both pixel- and region-based fusion approaches. Experimental results via computer simulation illustrate the improved performance of the proposed image fusion algorithms by both qualitative and quantitative means.