Deep Image

Deep image priors leverage the inherent structure of untrained convolutional neural networks to solve inverse problems in image processing, offering a powerful alternative to traditional methods and supervised deep learning approaches. Current research focuses on improving the stability and efficiency of these priors, addressing issues like overfitting and slow optimization through techniques such as self-reinforcement, variational methods, and meta-learning, often combined with generative models or other regularization strategies. This approach has significant implications for various fields, enabling improved image reconstruction in applications ranging from medical imaging and microscopy to remote sensing and video processing, particularly where training data is scarce or unavailable.