This repository contains Jupyter Notebooks that reproduce figures from the CVPR 2018 Deep Image Prior paper. The approach uses the structure of a convolutional network as a hand-designed prior for image restoration tasks such as denoising, inpainting, and super-resolution. Unlike typical deep learning methods, no external training data or learned weights are required—the optimization starts from random initialization and leverages the inductive bias inherent in the network architecture.