Int. J. Advanced Networking and Applications 5769 Volume:15 Issue:01 Pages: 5769 - 5774 (2023) ISSN: 0975-0290 Optimizing Image Fusion Using Modified Principal Component Analysis Algorithm and Adaptive Weighting Scheme Gargi Trivedi 1 Email: gargi1488@gmail.com Dr. Rajesh Sanghvi 2 Email : rajeshsanghvi@gcet.ac.in 1,2 Department of Applied Science & Humanities, G H Patel College of Engineering & Technology, CVM University, Vallabh Vidhyanagar-388120, India ---------------------------------------------------------------------- ABSTRACT-------------------------------------------------------------- Image fusion is an important technique for combining two or more images to produce a single, high-quality image. Principal component analysis (PCA) is a commonly used method for image fusion. However, existing PCA-based image fusion algorithms have some limitations, such as sensitivity to noise and poor fusion quality. In this paper, we propose a modified PCA algorithm for image fusion that uses an adaptive weighting scheme to improve the fusion quality. The proposed algorithm optimizes the fusion process by selecting the principal components that contain the most useful information and weighing them appropriately. Experimental results show that the proposed algorithm outperforms existing PCA-based image fusion algorithms in terms of fusion quality, sharpness, and contrast. Keywords - Image fusion, principle components analysis, adaptive weighting scheme, optimization, fusion quality, sharpness,contrast. ----------------------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: April 13, 2023 Date of Acceptance: May 28, 2023 ----------------------------------------------------------------------------------------------------------------------------------------------------- 1. INTRODUCTION Image fusion is a critical task in computer vision that involves combining multiple images of the same scene into a single, high-quality image that contains more information than any of the individual [1,2]. Principal component analysis (PCA) is a widely used method for image fusion that has been shown to be effective in many applications. However, existing PCA-based image fusion algorithms have some limitations, such as sensitivity to noise and poor fusion quality [3,4]. To address these limitations, this paper proposes a modified PCA algorithm for image fusion that uses an adaptive weighting scheme to improve the fusion quality. The proposed algorithm optimizes the fusion process by selecting the principal component that contains the most useful information and weighting them appropriately. The use of an adaptive weighting scheme ensures that the weight assigned to each principal component is based on its contribution to the final fused image, resulting in improved fusion quality. We also present experimental results that show the performance of the proposed algorithm compared to existing PCA-based image fusion algorithms. Our results demonstrate that the proposed algorithm outperforms existing algorithms in terms of fusion quality, sharpness, and contrast. The rest of the paper is organized as follows: • Section 2 provides a brief overview of existing image fusion techniques, • Section 3 describes the proposed modified PCA algorithm in detail, • Section 4 presents the experimental setup and results, and • Section 5 concludes the paper and discusses future work’s mention the main goal of the work and highlight the major conclusions. 2. OVERVIEW OF EXISTING IMAGE FUSION TECHNIQUES Image fusion is the process of combining multiple images of the same scene into a single, high-quality image that contains more information than any of the individual images. There are many image fusion techniques that have been developed to achieve this goal. These techniques can be broadly categorized into two categories: transform-based methods [5] and spatial domain methods [6]. Transform-based methods involve transforming the input images into a different domain, such as the frequency domain, and then combining them in that domain. One of the most commonly used transform-based methods is wavelet transform. Wavelet transform decomposes an image into multiple levels of different frequency bands, and then combines the high-frequency bands of the input images to create the fused image. Another commonly used transform- based method is discrete cosine transform (DCT) [7], which converts the image into a set of frequency coefficients and then combines them to create the fused image Spatial domain methods, on the other hand, operate on the images in their original spatial domain. These methods involve manipulating the pixel values of the input images to create the fused image. One of the most commonly used spatial domain methods is the pyramid-based method [8].In this method, the input images are decomposed into multiple levels of pyramids, and then the corresponding pyramid levels are combined to create the fused image. Another spatial domain method is the intensity-hue-saturation (IHS) method [9], which separates the input images into intensity,