Deep Image Prior

Deep Image Prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural-network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image statistics is captured by the structure of a convolutional image generator rather than by any previously learned capabilities.