Fast quantitative correlation analysis and information deviation analysis for evaluating the performances of image fusion techniques

Image fusion is becoming one of the hottest techniques in image processing. Its performance evaluation method that can compare and analyze different fusion techniques is an essential part of image fusion techniques. In this paper, we proposed two intuitive schemes-correlation and information deviation schemes-for evaluating the performances of image fusion techniques. The former scheme is a fast and compact version of the quantitative correlation analysis (QCA). The latter scheme is an information deviation analysis. Using the two schemes, the performances of different fusion techniques can be compared directly and quantitatively based on the source images and the fused images with a faster speed than the QCA method. Two multiresolution analysis-based image fusion methods, wavelet transform-based fusion and pyramid transform-based fusion, which operate on typical hyperspectral image sets [airborne visible/infrared imaging spectrometer (AVIRIS)], are evaluated by the proposed schemes. The simulation results show the correctness and effectiveness of the proposed two schemes.