Enhancing Image Reconstruction Accuracy Using Artificial Bee Colony (ABC) Optimization

In image reconstruction, it is significant to enhance the quality and the precision of the reconstructed images because it can be used for a diverse of fields, including medical diagnosis and satellite imagery interpretation. This paper focuses on the use of Artificial Bee Colony Optimization ABC, an optimization algorithm that is based on swarm intelligence with a view of improving image reconstruction algorithms. The work seeks to find out how ABC Optimization can be applied in optimizing the reconstruction of images from noisy data. This work has endeavored to provide a systematic comparison of performance outcomes of ABC Optimization as compared to the conventional and current techniques used in reconstruction of images such as MRI Consequently, this study establishes higher image quality and time savings when using the ABC Optimization algorithm. The experimental outcome of this research through case studies in illustrations such as in medical imaging, and remote sensing show that ABC optimization offers better results for lower reconstruction error and better detail preservation. In this work, authors give a detailed evaluation of the effectiveness of the ABC Optimization algorithm in image reconstruction and discuss possibilities for further development of this algorithm