The DOI matches its Crossref record.
Gradient domain high dynamic range compression
Abstract
We present a new method for rendering high dynamic range images on conventional displays. Our method is conceptually simple, computationally efficient, robust, and easy to use. We manipulate the gradient field of the luminance image by attenuating the magnitudes of large gradients. A new, low dynamic range image is then obtained by solving a Poisson equation on the modified gradient field. Our results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast. The method is also able to significantly enhance ordinary images by bringing out detail in dark regions.
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Citations
Cited by
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
Pérez, P., Gangnet, M., & Blake, A. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882269 · Cited by 1,773
Zhang, J., Cao, Y., Wang, Y., Wen, C., & Chen, C. W. (2018) · Proceedings of the 26th ACM international conference on Multimedia · doi:10.1145/3240508.3240653 · Cited by 40
Metadata
- Authors
- Fattal, R., Lischinski, D., & Werman, M.
- Venue
- ACM Transactions on Graphics
- Published
- 2002-07
- Volume
- 21
- Issue
- 3
- Pages
- 249-256
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2997077506
- Cited by
- 777
- References
- 0
- FWCI
- 19.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Gradient domain high dynamic range compression. https://app.cochat.ai/papers/10.1145/566654.566573
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- Fattal, R., Lischinski, D., & Werman, M. (2002). Gradient domain high dynamic range compression. ACM Transactions on Graphics. https://doi.org/10.1145/566654.566573
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
