DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.
- DOI
- 10.1038/s44172-025-00582-4
- Published
- 2026 Jan 12
- Container
- Communications engineering
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1038/s44172-025-00582-4,
title = {DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.},
author = {Li R and Yushkevich A and Chu X and Kudryashev M and Yakimovich A},
year = {2026},
journal = {Communications engineering},
doi = {10.1038/s44172-025-00582-4},
url = {https://doi.org/10.1038/s44172-025-00582-4}
}RIS
TY - JOUR TI - DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems. AU - Li R AU - Yushkevich A AU - Chu X AU - Kudryashev M AU - Yakimovich A PY - 2026 JO - Communications engineering DO - 10.1038/s44172-025-00582-4 UR - https://doi.org/10.1038/s44172-025-00582-4 ER -
APA
R, L., A, Y., X, C., M, K., & A, Y. (2026). DeBCR: a sparsity-efficient framework for image enhancement through a deep-learning-based solution to inverse problems.. Communications engineering. https://doi.org/10.1038/s44172-025-00582-4
Source records
- pubmed · retrieved 2026-11-10T14:04:40.058Z