Multi-Focus Image Fusion using 2D Compact Variational Mode Decomposition

Fusion is the technique to integrate noteworthy information from different devices such as sensors using mathematical techniques. The major motive of image fusion is to generate an image that has maximized information and clarity while maintaining integrity. Image fusion aims to reduce redundant data and to construct image more eligible and comprehensible for perception. Fusion is now an indispensable part of technical and industrial applications. This paper investigate the significance of 2D Compact Variational Mode Decomposition (2D-CVMD) algorithm for multi focus fusion of image. The methodology proposed emphasises on multi-focus images. Through extensive experiments it is validated that the proposed methodology generates final fused image with maximum information and better explanation of the view than the original images. Further the accuracy of estimation is validated using standard quality metrics on different multi-focus fusion images.

I. Introduction

The fusion technique’s main objective is to integrate details from various images of the same setting, into a new image that incorporates every information from each of the source images without any discrepancies [1]. Due to the focal length limitations of camera lenses, capturing an image that has focus on all the objects is not possible. This results in multi-focus images [2]. Image fusion offers image sharpening, feature augmentation, enhanced categorization, and the production of stereo data sets. Image fusion on multi-focus images is the method of merging essential details from multiple images to generate a single, fine image. Image fusion is used in a variety of applications, including medical imaging, remote sensing, and surveillance.