An optimized block-based intuitionistic fuzzy framework for multi-focus image fusion.

Introduction: Multi-focus image fusion aims to generate a single all-in-focus image by combining multiple images captured at different focal depths. However, accurately identifying focused and blurred regions remains challenging due to real-world focus transitions and the uncertainties inherent between sharp and defocused areas. Methods: To address these limitations, this article presents a new multi-focus image fusion method employing a novel intuitionistic fuzzy generator. In the proposed framework, an input image is initially converted into an intuitionistic fuzzy image (IFI), followed by a fusion rule termed the optimized block-based partitioning and defocusing synthesis algorithm to generate the final fused image. The utilization of IFIs facilitates the management of uncertainties associated with the membership and non-membership degrees of an image. Results: The proposed fusion algorithm demonstrates superior performance compared with existing state-of-the-art methods in terms of entropy, average gradient, and spatial frequency. Discussion: Analytical experiments and comparative evaluations demonstrate that the proposed approach achieves improved visual quality and effectively addresses the uncertainties associated with focused and defocused regions in multi-focus image fusion.