Fusion of infrared and visible images based on the second generation Curvelet transform and MPCA

Abstract

For the fusion problem of infrared and visible light images with the same scene, an image fusion algorithm based on the second generation Curvelet and Modular Principal Component Analysis (MPCA) was proposed. Firstly, the fast discrete Curvelet transform was performed on the original images to obtain coarse scale and fine scale coefficients at different scales and in various directions respectively. Secondly, according to the different physical features of infrared and visible light images and human visual system features, the fusion weights were determined by MPCA method for coarse scale coefficients; while the fusion rule based on local region energy was used for fine scale coefficients. Finally, the fusion results were obtained through the inverse Curvelet transform. The experimental results illustrate that the proposed algorithm is effective for extracting the characteristics of the original images and has better fusion results than others in the subjective visible effect and objective evaluation index.