A Comprehensive Review of the Use of Cuckoo Search Algorithm in Digital Imaging: Trends, Challenges, and Prospects

Abstract

Abstract

This study explores the application of the cuckoo search (CS) algorithm in digital image processing, emphasizing its efficacy as a robust optimization technique for complex imaging tasks. The research investigates the algorithm’s capability to enhance image segmentation, denoising, and feature extraction, particularly within noisy and high-dimensional data environments. Hybrid models integrating CS with deep neural networks and auxiliary metaheuristics demonstrate significant improvements in convergence speed, accuracy, and computational efficiency. Comparative analyses highlight CS’s advantages over traditional algorithms and other metaheuristics, underscoring its adaptability and scalability across diverse imaging modalities. Extensive experimental results, supported by detailed tables, validate the superiority of hybridized CS approaches in medical imaging, remote sensing, and real-time applications. The findings advocate for continued innovation through parameter adaptation and integration with emerging AI paradigms, including multimodal data fusion and quantum computing. Overall, this research confirms the potential of the CS algorithm, especially in hybrid frameworks, as a foundational tool for advancing autonomous, high-precision image analysis systems in scientific and industrial contexts.