Fusion Algorithm for Near-Infrared and Color Visible Images Based on Tetrolet Transform

Abstract

To address the problems of low contrast,loss of detail,and color distortion after near-infrared and color visible image fusion,an algorithm for near-infrared and color visible image fusion based on the Tetrolet transform and pulse coupled neural network(PCNN)is proposed.First,the color visible light source image is transformed into a hue saturation intensity space,where each component is relatively independent,and its brightness component is decomposed into a near-infrared image by Tetrolet decomposition.Subsequently,a fusion rule for expectation maximization likelihood estimation of the potential distribution from a given incomplete dataset is proposed.A self-adaptive PCNN model with a Sobel operator that automatically adjusts the threshold is used as a fusion rule,and the processed high-frequency and low-frequency images are fused by Tetrolet inverse transformation as brightness images.An adaptive stretching method for the saturation component is proposed to solve the problem of image saturation decline.The processed components are mapped back to red–green–blue space to complete the fusion.The proposed algorithm was compared with several efficient fusion algorithms.The experimental results show that the image obtained by this method has clear details and improved color contrast.It has obvious advantages in image saturation,color restoration performance,structural similarity,and contrast.