Image Noise

Image noise, unwanted variations in pixel values, degrades image quality and hinders accurate analysis across diverse applications, from microscopy to autonomous driving. Current research focuses on developing effective denoising techniques, employing deep learning models like Variational Autoencoders (VAEs), diffusion probabilistic models (DDPMs), and convolutional neural networks (CNNs), often incorporating metadata or leveraging self-supervised learning to address various noise types and correlations. These advancements are crucial for improving the reliability of image-based analyses in numerous scientific fields and technological applications, ranging from medical imaging to computer vision.