Underwater image enhancement using hybrid transformers and evolutionary particle swarm optimization

Abstract

Underwater imaging is a complex task due to inherent challenges such as limited visibility, color distortion, and light scattering in the water medium. To address these issues and enhance underwater image quality, this research presents a novel framework based on a Hybrid Transformer Network optimized using Particle Swarm Optimization (HTN-PSO). The HTN-PSO framework combines the strengths of convolutional neural networks and transformer models to effectively capture low-level features and model long-range dependencies. Simultaneously, PSO optimizes the transformer’s parameters to maximize the enhancement quality of underwater images. The proposed framework consists of four main stages: data augmentation, pre-processing, feature extraction using HTN-PSO, and enhanced image reconstruction. The performance of HTN-PSO is evaluated using objective quality metrics such as UIQM, NIQE, and BRISQUE, along with subjective assessments. The proposed model has been evaluated using HTN-PSO on four benchmark datasets: RUIE, EUVP, UWGAN, and UIEB and reports improvements over existing methods. Notably, HTN-PSO achieves a 12% increase in UIQM and up to 15% reduction in BRISQUE compared to baseline techniques, including Uformer and Restormer. Experimental results demonstrate the superiority of the HTN-PSO approach over both traditional and neural network-based methods, offering a promising avenue for improving underwater image enhancement across various domains, including exploration, research, and surveillance applications.