Abstract
Image fusion technology usually combines information from multiple images of the same scene into a single image so that the fused image is often more informative than any source image. Considering the characteristics of low-light visible images, this study presents an image fusion technology to improve contrast of low-light images. This study proposes an adaptive threshold-based fusion rule. Threshold is related to the brightness distribution of original images. Then, the fusion of low-frequency coefficients is determined by threshold. Pulse-coupled neural networks (PCNN)-based fusion rule is proposed for fusion of high-frequency coefficients. Firing times of PCNN reflect the amount of detail information. Thus, a high-frequency coefficient corresponding to maximum firing times is chosen as the fused coefficient. Experimental results demonstrate that the proposed method obtains high-contrast images and outperforms traditional fusion approaches on image quality.
Highlights
Image fusion technology is aimed at obtaining one image with high quality from two more images of the same scene.[1]
For the traditional contourlet transform-based fusion method, low-frequency coefficients are fused by average rule and high-frequency coefficients are fused by absolute value choosing max rule
Different fusion rules are chosen based on characteristics of low- and high-frequency information
Summary
Image fusion technology is aimed at obtaining one image with high quality from two more images of the same scene.[1]. It is necessary to study fusion technology of infrared and low-light visible images. In most pulse-coupled neural networks (PCNN)based fusion algorithms, only the single pixel value is used to motivate a PCNN neuron It is not effective enough because human eyes are usually sensitive to features. Orientation information is considered as a feature to motivate PCNN.[11] This algorithm can preserve spatial characteristics of source images well and only grayscale source images are considered in this method. Traditional fusion methods are not suitable for fusion of infrared and low-light visible images. To overcome these problems, a fusion technology is presented in this paper. Piao, and Tahir: Research on fusion technology based on low-light visible image and infrared image
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