Hybrid Technique for the Image Fusion Detection

Abstract

Image processing is the technique in which information is stored in the form of pixels. Image fusion is the approach of image processing that extracts useful information from the images. In the last few years, image fusion has got the attention of researchers for its ability to yield results that inherit the best qualities from all the sources. Just like fusion, using a hybrid of a variety of algorithms can exhibit the most significant effects.  Mostly, the researchers opt for transformation techniques. It is analyzed from the results that transformation techniques are unable to extract all relevant information from the images. In this research work, we are proposing hybrid image fusion techniques to obtain all relevant information from the images. The hybrid method is the combination of ACO and PSO algorithm and has been implemented on MATLAB. These swarm intelligence algorithms are nature-inspired and are robust in solving complex problems. PSO tends to find the optimum pixel value in the extracted features, and these values are used as input for the ACO algorithm, which tries to find the optimal solution for a variety of issues in an image like edge detection by choosing the smallest route based on attractiveness and route update. The proposed hybrid algorithm is able to achieve high PSNR value and low MSE value for the image fusion technique.