Noise-Aware Metaheuristic Optimization of Non-Local Means Denoising via a Ratel Optimization Algorithm

Classical image denoising methods remain relevant in practical scenarios where training data or noise models are unavailable, yet their performance is highly sensitive to parameter selection. Non-Local Means (NLM) is a representative example whose effectiveness depends critically on smoothing strength, patch size, and search window configuration. This paper formulates NLM parameter selection as a black-box optimization problem under unknown noise conditions and employs adaptive metaheuristic optimization strategies for this task. We propose an adaptive optimization framework that integrates rank-based perturbation, opposition-based learning, Lévy-flight exploration, and noise-aware parameter constraints to improve robustness and convergence. The proposed method is evaluated against fixed-parameter NLM and NLM optimized using standard evolutionary algorithms under identical protocols. Experiments on three sets of datatset demonstrate consistent improvements in PSNR and SSIM, highlighting the continued relevance of adaptive optimization for classical denoising.