Lightweight Infrared and Visible Image Fusion Technique: Guided Gradient Optimization Driven

Infrared and visible image fusion technology aims to combine data from several spectral bands in order to increase target identification, processing capabilities, and image quality. With the rapid development of consumer electronic products for imaging, there is an urgent need for a lightweight and efficient fusion technology that ensures efficient information extraction and fusion while maintaining image quality. Existing algorithms designed to achieve accurate information extraction, noise reduction, artefact suppression, and edge preservation need to be simplified and more challenging to meet the requirements of lightweight imaging consumer electronic products. We propose a lightweight method for the fusion of infrared and visible images by exploiting the properties of the AnisGF and gradient filters. This method achieves significant feature texture extraction, effectively reduces gradient texture and noise, minimizes halo artifacts, and enhances edge contours while preserving overall image brightness and edge gradients. Furthermore, the explicit stage processing and concise algorithmic structure design of this method contribute to its optimal time efficiency. Experimental results demonstrate its superiority in both subjective visual effects and objective metrics over nine other existing image fusion methods.