The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Deep Image Prior
Open accessAbstract
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity (Code and supplementary material are available at https://dmitryulyanov.github.io/deep_image_prior).
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Cites
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RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., & Van Gool, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01117 · Cited by 1,778
SinGAN: Learning a Generative Model From a Single Natural Image
Shaham, T. R., Dekel, T., & Michaeli, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00467 · Cited by 890
A survey of human-in-the-loop for machine learning
Wu, X., Xiao, L., Sun, Y., Zhang, J., Ma, T., & He, L. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.05.014 · Cited by 800
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
Liu, R., Lehman, J., Molino, P., Such, F. P., Frank, E., Sergeev, A., & Yosinski, J. (2018) · arXiv · doi:10.48550/arxiv.1807.03247 · Cited by 784
Deep learning in computer vision: A critical review of emerging techniques and application scenarios
Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100134 · Cited by 754
Metadata
- Authors
- Ulyanov, D., Vedaldi, A., & Lempitsky, V.
- Venue
- International Journal of Computer Vision
- Published
- 2020-03-04
- Volume
- 128
- Issue
- 7
- Pages
- 1867-1888
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2771305881
- Cited by
- 719
- References
- 63
- FWCI
- 37.97
- Type
- journal article
- Language
- en
- Open access
- green
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