An Infrared and Visible Images Fusion Algorithm Based on Wavelet Multi-Resolution Decomposition

Abstract

Image fusion can be effectively utilized to obtain image redundant information from sensors, hereby improving the accuracy and reliability of information. Based on multi-resolution decomposition of the traditional image fusion method is vulnerable to high frequency noise, fusion is often ineffective. An improved image fusion algorithm has been studied based on the wavelet multi-resolution decomposition. The principle of the algorithm is regional energy maximum for low frequency decomposition image, and the bivariate statistical model for high frequency part. Experimental results show that the bivariate statistical model for the high frequency band is robust to noise based on the joint probability of wavelet coefficient in the conditions of Daubechies wavelet basis function with decomposing level 5 multi-resolution decomposition. Simultaneously, the regional energy maximum for low frequency band can be effective on the high frequency band based on the bivariate statistical model. Fusion image have a larger contrast, the preferred details and the higher gray level resolution.