A Comparative Study in Image Fusion Using Orthogonal and Biorthogonal Wavelet

A method called picture fusion is largely concerned with improving photographs to enhance scene visualization. In order to produce a composite image that is more significant and instructive, it attempts to maintain the key elements from each image. This method has been used in a number of industries, including robots, satellite images, and medicine. Researcher decision-making and the success of scientific endeavors are greatly influenced by the quality of the fused image. In this article, a comparison of the wavelets Haar and Bior 2.2 is made. In order to compare the two wavelets, different photos are employed. Based on metrics like the Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Standard Deviation (SD), the performance of the wavelets is assessed. Based on the results, it can be said that Bior 2.2 performs somewhat better than Haar in terms of picture fusion quality. We use orthogonal and biological wavelets to compare how well image fusion works. To fuse pictures, we utilize the Discrete Wavelet Transform (DWT) and the maximum selection criteria. The performance of the fused pictures is assessed using a number of factors, such as visual quality measurements. The advantages of biorthogonal wavelets over orthogonal wavelets for image fusion tasks are highlighted in this research, especially when working with numerous sensors. It emphasizes how important the decomposition level is for producing high-quality fused pictures. The results help advance image fusion methodologies and offer insightful information to academics and industry professionals involved in picture processing and analysis.