Deep Image Prior

Deep Image Prior (DIP) leverages the inherent inductive biases of untrained convolutional neural networks to solve inverse problems in image processing, offering a powerful alternative to traditional methods and supervised deep learning approaches that require extensive training data. Current research focuses on improving DIP's stability and efficiency through modifications like stochastic iterations, incorporation of additional priors (e.g., total variation, Bloch consistency), and architectural innovations (e.g., U-Net, bagged DIP). This unsupervised learning framework has shown promise in diverse applications, including medical imaging (MRI, CT), remote sensing, and computational photography, by enabling high-quality image reconstruction from limited or noisy data.