FusionNGFPE: An image fusion approach driven by non-global fuzzy pre-enhancement framework

Abstract

The majority of prevailing image fusion methods employ a global strategy, often resulting in a reduction of contrast. This study addresses this issue by proposing a novel image fusion approach called FusionNGFPE, specifically designed for the structural characteristics of infrared (IR) imagery. The approach introduces a contrast equalization algorithm based on the Fourth-order Partial Differential Equation (FPDE) to enhance background regions effectively. Considering the inherent differences between IR and visible (VIS) images, we developed a hybrid fusion strategy that combines the Expectation Maximization (EM) algorithm and Principal Component Analysis (PCA). Comparative analysis with state-of-the-art fusion methods shows that our proposed algorithm achieves superior performance in both qualitative and quantitative evaluations. To further demonstrate the practical significance of FusionNGFPE, we integrated this fusion framework into the RGBT target tracking task using the VOT-RGBT and OTCBVS datasets. Extensive comparative experiments confirm that the FusionNGFPE framework integrates seamlessly with the tracking task, significantly improving tracking accuracy across diverse scenarios.