Image Fusion Using Wavelet Transformation and XGboost Algorithm

Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device diagnostics, and intelligent assembly line robots, with image quality varying depending on application. The research paper presents various methods for merging images in spatial and frequency domains, including a blend of stable and curvelet transformations, everage Max-Min, weighted principal component analysis (PCA), HIS (Hue, Intensity, Saturation), wavelet transform, discrete cosine transform (DCT), dual-tree Complex Wavelet Transform (CWT), and multiple wavelet transform. Image fusion methods integrate data from several source images of an identical target, thereby enhancing information in an extremely efficient manner. More precisely, in imaging techniques, the depth of field constraint precludes images from focusing on every object, leading to the exclusion of certain characteristics. To tackle thess challanges, a very efficient multi-focus wavelet decomposition and recomposition method is proposed. The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice. The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images. This study enhances the performance of the eXtreme Gradient Boosting (XGBoost) algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection. The performance of images is improved by segmenting them employing the K-Means algorithm. The segmentation method aids in identifying specific regions of interest, using Particle Swarm Optimization (PCA) for trait selection and XGBoost for data classification. Extensive trials confirm the model’s exceptional visual performance, achieving an accuracy of up to 97.067% and providing good objective indicators.