Dorian Yao Chan
James F. O'Brien
ABSTRACT
We present a new spatially-varying dynamic range compression algorithm for high dynamic range (HDR) images based on bound-constrained optimization using soft constraints. Rather than explicitly attenuating gradients as in previous work, we minimize an objective function to instead compute a globally optimal manipulation of input pixel differences. Our framework provides simple yet effective preservation of visually important image properties, such as order statistics and global consistency, that requires little to no parameter tuning. Our results are free of haloing, washout, and other artifacts, while retaining detail across the image’s full range. The speed of our algorithm and flexibility of the constraint framework allows our method to be easily extended to video.
Supplemental Material
Available for Download
- Raanan Fattal, Dani Lischinski, and Michael Werman. 2002. Gradient Domain High Dynamic Range Compression. ACM Trans. Graph. 21, 3 (July 2002), 249–256.Google Scholar
Digital Library
- Rafal Mantiuk, Karol Myszkowski, and Hans-Peter Seidel. 2006. A Perceptual Framework for Contrast Processing of High Dynamic Range Images. ACM Trans. Appl. Percept. 3, 3 (July 2006), 286–308.Google ScholarDigital Library
- Sylvain Paris, Samuel W. Hasinoff, and Jan Kautz. 2011. Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid. ACM Trans. Graph. 30, 4, Article 68 (July 2011), 12 pages.Google ScholarDigital Library
- Xiao Shu and Xiaolin Wu. 2018. Locally Adaptive Rank-Constrained Optimal Tone Mapping. ACM Trans. Graph. 37, 3, Article 38 (July 2018), 10 pages.Google ScholarDigital Library
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