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
# Resources
# References
- #PAPER Deep Image Prior (Ulyanov 2018)
- #PAPER A Bayesian Perspective on the Deep Image Prior (Cheng 2019)
- #PAPER Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior (Laves 2020)
- #PAPER A Mean-Field Variational Inference Approach to Deep Image Prior for Inverse Problems in Medical Imaging (Tolle 2021)