The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Non-rigid dense correspondence with applications for image enhancement
Abstract
This paper presents a new efficient method for recovering reliable local sets of dense correspondences between two images with some shared content. Our method is designed for pairs of images depicting similar regions acquired by different cameras and lenses, under non-rigid transformations, under different lighting, and over different backgrounds. We utilize a new coarse-to-fine scheme in which nearest-neighbor field computations using Generalized PatchMatch [Barnes et al. 2010] are interleaved with fitting a global non-linear parametric color model and aggregating consistent matching regions using locally adaptive constraints. Compared to previous correspondence approaches, our method combines the best of two worlds: It is dense, like optical flow and stereo reconstruction methods, and it is also robust to geometric and photometric variations, like sparse feature matching. We demonstrate the usefulness of our method using three applications for automatic example-based photograph enhancement: adjusting the tonal characteristics of a source image to match a reference, transferring a known mask to a new image, and kernel estimation for image deblurring.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
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Adaptive histogram equalization and its variations
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A comparison of affine region detectors
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Cited by
GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence
Bian, J., Lin, W.-Y., Matsushita, Y., Yeung, S.-K., Nguyen, T.-D., & Cheng, M.-M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.302 · Cited by 594
Light Field Image Processing: An Overview
Wu, G., Masia, B., Jarabo, A., Zhang, Y., Wang, L., Dai, Q., Chai, T., & Liu, Y. (2017) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2017.2747126 · Cited by 586
Efficient Dense-Field Copy–Move Forgery Detection
Cozzolino, D., Poggi, G., & Verdoliva, L. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2455334 · Cited by 451
Robust patch-based hdr reconstruction of dynamic scenes
Sen, P., Kalantari, N. K., Yaesoubi, M., Darabi, S., Goldman, D. B., & Shechtman, E. (2012) · ACM Transactions on Graphics · doi:10.1145/2366145.2366222 · Cited by 433
Self-citationRobust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach
Ma, K., Li, H., Yong, H., Wang, Z., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2671921 · Cited by 355
Metadata
- Authors
- HaCohen, Y., Shechtman, E., Goldman, D. B., & Lischinski, D.
- Venue
- ACM SIGGRAPH 2011 papers
- Publisher
- ACM
- Published
- 2011-07-25
- Pages
- 1-10
- OpenAlex
- W2106505277
- Cited by
- 287
- References
- 33
- FWCI
- 17.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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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.
