FASO-C: A rapid visualization technique based on optimized fusion with crossover-based atom search for multi-band imagery

Abstract

Multi-band image fusion is a vital image processing technique used to combine data from multiple images or sensor bands, particularly in scenarios where a wide range of spectral information is available. Its primary objective is to create a single, comprehensive image that not only visually enhances the scene but also preserves critical details and characteristics present in each source. This paper proposes a novel image fusion technique that combines the useful features from a set of source images to provide a quick visualization of the scene. The approach is highly beneficial for multi-band images, including a large number of bands acquired by sampling the wavelength spectrum at narrow intervals. The fusion process depends on a multi-objective cost function, which optimizes the resultant fused image according to specific desired characteristics. By solving this optimization framework, an optimal set of weights is obtained using a novel Fusion-Driven Atom Search Optimization with Crossover (FASO-C) algorithm for the fusion of the image bands. Crossover operation enhances the exploitation capability of ASO, enabling the global optimal solution to be achieved quickly. To further validate the effectiveness of the proposed fusion model, various experiments were conducted to assess its performance using both subjective and objective metrics, as well as analyze its convergence.