The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
A Deep Journey into Super-resolution: A Survey
Abstract
Deep convolutional networks–based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare more than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep learning–based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed shows the consistent and rapid growth in the accuracy in the past few years along with a corresponding boost in model complexity and the availability of large-scale datasets. It is also observed that the pioneering methods identified as the benchmarks have been significantly outperformed by the current contenders. Despite the progress in recent years, we identify several shortcomings of existing techniques and provide future research directions towards the solution of these open problems. Datasets and codes for evaluation are publicly available at https://github.com/saeed-anwar/SRsurvey.
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Cites
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Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology
Zhang, D., Schroeder, A., Yan, H., Yang, H., Hu, J., Lee, M. Y. Y., Cho, K. S., Susztak, K., Xu, G. X., Feldman, M. D., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02019-9 · Cited by 219
Grotte, M. E., Birkeland, R., Honore-Livermore, E., Bakken, S., Garrett, J. L., Prentice, E. F., Sigernes, F., Orlandic, M., Gravdahl, J. T., & Johansen, T. A. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3080175 · Cited by 91
Metadata
- Authors
- Anwar, S., Khan, S., & Barnes, N.
- Venue
- ACM Computing Surveys
- Published
- 2020-05-28
- Volume
- 53
- Issue
- 3
- Pages
- 1-34
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3040726448
- Cited by
- 394
- References
- 72
- FWCI
- 19.37
- Type
- journal article
- Language
- en
- Open access
- closed
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