Denoising Approach

Denoising approaches aim to remove unwanted noise from various data types, including images, time series, and biosignals like electromyography (EMG), improving data quality for analysis and application. Current research emphasizes both traditional methods like BM3D and novel deep learning architectures such as U-Net and Transformers, often combined with contrastive learning or flow-based models for improved performance and robustness across diverse noise types. The effectiveness of these methods is increasingly evaluated not only by image fidelity metrics but also by task-based performance, highlighting the importance of aligning denoising strategies with specific downstream applications in fields ranging from medical imaging to computer vision.