The DOI matches its Crossref record; 154 of the 154 references it cites were found in a registry or an index.
Deep Learning Techniques for Inverse Problems in Imaging
Abstract
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.
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Cites
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Auto-Encoding Variational Bayes
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GAN（Generative Adversarial Nets）
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Transformers in medical imaging: A survey
Shamshad, F., Khan, S., Zamir, S. W., Khan, M. H., Hayat, M., Khan, F. S., & Fu, H. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102802 · Cited by 1,296
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Fei, B., Lyu, Z., Pan, L., Zhang, J., Yang, W., Luo, T., Zhang, B., & Dai, B. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00958 · Cited by 279
Deblurring via Stochastic Refinement
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., & Milanfar, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01581 · Cited by 259
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Metadata
- Authors
- Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., & Willett, R.
- Venue
- IEEE Journal on Selected Areas in Information Theory
- Published
- 2020-05
- Volume
- 1
- Issue
- 1
- Pages
- 39-56
- ISSN
- 2641-8770 (ISSN-L)
- OpenAlex
- W3021094251
- Cited by
- 577
- References
- 154
- FWCI
- 32.32
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., & Willett, R. (2020). Deep Learning Techniques for Inverse Problems in Imaging. IEEE Journal on Selected Areas in Information Theory. https://doi.org/10.1109/jsait.2020.2991563
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