A Hybrid Bioinspired Approach for Advanced Image Reconstruction

With the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processing. This paper proposes a new hybrid approach for reconstructing images using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Wavelet Fusion (WF) techniques. While PSO seeks a solution modeled on the flocking and schooling patterns in birds and fish, GA helps to explore the solution space, by reducing the risk of local minima as well as improving the process of searching by modeling specific natural mechanisms at play in evolution, and WF enhances image quality by lessening noise. This is considered as the major contribution of the work, which lies in the bio-inspired algorithm by integrating swarm intelligence and wavelet fusion techniques applied to multiple initial reconstruction steps for an image to approximate the intended reconstructed one. Experimental results show that this hybrid approach converges fast and gives better reconstruction with a low Mean Squared Error (MSE). The proposed methodology provides a strong foundation for developing image reconstruction techniques by demonstrating that swarm intelligence can be integrated with wavelet-based techniques.