Noisy Image

Noisy image processing focuses on restoring or enhancing images degraded by various noise sources, aiming to improve visual quality and enable accurate analysis. Current research emphasizes developing efficient and robust algorithms, often employing deep learning models like convolutional neural networks (CNNs), diffusion models, and neural ordinary differential equations (NODEs), sometimes integrated with techniques like contrastive learning and Radon transforms. These advancements are crucial for diverse applications, including medical imaging, remote sensing, and autonomous systems, where reliable image analysis is paramount despite noisy data acquisition. The field is actively exploring self-supervised and unsupervised learning approaches to reduce reliance on large, paired datasets of clean and noisy images.