The use of Bendlets, Fuzzy and Modified Spatial Frequency for Image Fusion

Image fusion's end goal is to create a composite image from numerous input images. One of the most popular discrete mathematical methods for picture analysis and synthesis is the Discrete Wavelet Transform, or DWT. The resulting image quality is reduced by existing DWT based image fusion technologies, which experience undesired side effects such as blurring and lags behind the accompanying data. The Bendlet transform approach, which is part of this family, is superior at merging multifocus images into a single composite image in real-time systems. Bendlets compromise numerous benefits, such as enhanced image precision, texture recognition, and tumor boundary delineation. Clarity is greatly enhanced with Modified Spatial Frequency (MSF). This study aims to improve visual analysis and reduce output image blurring by presenting a new method based on Bendlet transform and MSF. A weighting process is used to dynamically modify each modality's contribution according to image properties. In addition, to improve the accuracy of feature extraction and image reconstruction, the framework includes significant decomposition algorithms. Comparing the suggested method to current state-of-the-art methodologies, extensive testing on benchmark datasets shows significant enhancements in visual quality and quantitative measures. The approach described here provides a reliable, effective, and flexible image fusion solution that might improve patient care and improve diagnosis.