Fast Global Image Smoothing Based on Weighted Least Squares
By Dongbo Min, Sunghwan Choi, Jiangbo Lu, Bumsub Ham, K. Sohn, Minh N. Do. IEEE Transactions on Image Processing, 2014
355 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/4ae0ff2541513bc9cfad94a714464c3a37f10b0c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Ming-Hsuan Yang
- Dacheng Tao
- Trevor Darrell
- Alan L. Yuille
- Xiaogang Wang
- Andrea Vedaldi
In Media Technology
- Roberto Arroyo
- Ben Poole
- Gregory Hager
- Pablo Fernández Alcantarilla
- Simon Stent
- Riccardo Gherardi
- Gordon A. Christie
- Jonathan T. Barron
Citations in the corpus
- The Fast Bilateral Solver (2015, methodology): “The techniques with speeds comparable to or better than the bilateral solver [11, 41, 16, 31] produce error rates that are 25-40% greater than our approach.”
- Semantic Stereo for Incidental Satellite Images (2018, methodology): “respondence For pairwise stereo correspondence, we first considered a Semi-Global Matching (SGM) [19] implementation that incorporates weighted least squares (WLS) edgeaware - disparity map filtering [36]. This is available in OpenCV and runs quickly, providing a useful quick indicator of the challe”
- Street-view change detection with deconvolutional networks (2016, methodology): “The depth estimate is refined by means of a fast global smoother (FGS) based on weighted least squares as described in [26].”