denoisingneuroverkot
Denoising neural networks are a class of machine learning models designed to recover clean signals from noisy observations. They are widely used for image, audio, video, and biomedical data where noise degrades information or perceptual quality. The term encompasses architectures such as denoising autoencoders, convolutional denoisers, and modern diffusion-based models that learn to reverse a noising process.
Denoising autoencoders train a network to map a corrupted input to its original, clean version, typically using
Training objectives commonly minimize reconstruction error, such as mean squared error or perceptual losses, and may
Applications include improving photographic image quality, denoising medical images (MRI, CT), removing interference in audio signals,
Denoising neural networks are related to broader topics in image restoration, score-based models, and self-supervised learning.