Deep Learning Based Denoising

Deep learning-based denoising aims to improve the quality of noisy data across various modalities, including images (medical, hyperspectral), speech, and point clouds, by leveraging the power of neural networks to remove noise while preserving essential features. Current research emphasizes developing efficient architectures, such as diffusion models and those incorporating contextual information or curvature maps, to address computational constraints and improve generalization across different noise types and data characteristics. These advancements have significant implications for diverse fields, enhancing the accuracy and reliability of analyses in medical imaging, speech processing, and other scientific applications where noise reduction is crucial. The focus is shifting towards optimizing both performance and computational efficiency, leading to more practical and widely applicable solutions.