A Comprehensive Comparative Analysis of Image Restoration Algorithms: Performance Metrics and Insights

Abstract

This research paper presents a rigorous comparative analysis of five leading image restoration algorithms: Wiener Filter, Adaptive Histogram Equalization (AHE), Denoising through Non-Local Means (NLM), Iterative Back Projection (IBP), and Richardson-Lucy (RL) Deconvolution. With a focus on applications in medical imaging, surveillance, and remote sensing, the study addresses challenges related to noise and degradation. Our evaluation, conducted on a diverse dataset, employs key performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Universal Image Quality Index (UIQI). The research yields compelling evidence, positioning the Richardson-Lucy Deconvolution algorithm as the optimal choice. Demonstrating superior performance in high-quality image reconstruction, noise reduction, and structural preservation, RL Deconvolution emerges as the most suitable technique for a range of real-world scenarios. This research contributes pivotal insights, steering the practical application of image restoration towards heightened efficacy and reliability.