# Deep Image Prior

**Authors:** Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky  
**arXiv:** [1711.10925](https://arxiv.org/abs/1711.10925)  
**Date:** 2017-11-29  
**Summary page:** https://www.summarizepaper.com/en/paper/1711.10925v4/

## Key points

- Deep convolutional networks are used for image generation and restoration
- Generator network structure alone can capture low-level image statistics without learning
- Randomly-initialized neural network serves as a handcrafted prior for achieving excellent results in denoising, super-resolution, and inpainting
- The same prior can be used for diagnostic purposes and restoring images based on flash-no flash input pairs
- The framework bridges the gap between learning-based methods using deep convolutional networks and learning-free methods with handcrafted image priors
- Insights into neural networks' capabilities in capturing intrinsic image features without extensive training data
- Availability of code and supplementary material enhances accessibility and reproducibility of the research

## Summary

The paper "Deep Image Prior" by Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky explores the use of deep convolutional networks for image generation and restoration. These networks are known for their ability to learn realistic image priors from a large dataset of example images. However, the authors present a novel perspective by demonstrating that the structure of a generator network alone can capture significant low-level image statistics even before any learning takes place. This approach not only showcases the diverse applications of their method but also sheds light on the inductive bias inherent in standard generator network architectures. By utilizing a randomly-initialized neural network as a handcrafted prior, the authors achieve excellent results in various standard inverse problems such as denoising, super-resolution, and inpainting. They also show that this same prior can be effectively employed to invert deep neural representations for diagnostic purposes and to restore images based on flash-no flash input pairs. This comprehensive framework bridges the gap between learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors like self-similarity. The findings presented in this paper offer valuable insights into the capabilities of neural networks in capturing intrinsic image features without extensive training data. The availability of code and supplementary material further enhances the accessibility and reproducibility of their research. Overall, "Deep Image Prior" contributes significantly to advancing our understanding of image restoration techniques and highlights the potential of leveraging network structures for effective prior learning in various imaging tasks.