Evaluating Denoising Approaches for RGB-Infrared Images: Systematic Review and Comparative Analysis of Traditional Methods and Performance Metrics

Denoising enhances image quality by separating noise from observed signals, eliminating extraneous information while preserving essential features and image integrity. However, existing surveys on conventional denoising techniques often focus solely on processing-domain taxonomies, thereby neglecting evolutionary relationships, overlooking recent advances, and lacking multi-modal exploration. Consequently, modern machine learning pipelines have not fully exploited classical techniques. To advance multi-modal denoising and inspire new learning-based algorithms, this paper presents a comprehensive review of traditional denoising methods, quantitatively assesses their cross-modal transferability, and explores their integration into learning frameworks. Specifically, (1) this work proposes a novel taxonomy for conventional denoising techniques, including domain-based and signal-decomposition-based approaches, provides a systematic analysis of their evolutionary relationships, and investigates recent advances. (2) The study evaluates multi-modal denoising performance by applying baseline methods to infrared images and conducting a comparative analysis. (3) This paper surveys the latest research on traditional approaches, retraces their co-evolution with machine learning, and specifically explores the potential for fusing these techniques within learning-based denoising algorithms. In general, this review serves as a valuable reference for researchers in RGB-infrared denoising, image restoration, and related fields. The advancements in these areas significantly impact various domains, including defect detection in industrial production, worker protection safety recognition, and object tracking in smart transportation.