D
Dani Lischinski
Researcher at Hebrew University of Jerusalem
Publications - 158
Citations - 20287
Dani Lischinski is an academic researcher from Hebrew University of Jerusalem. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 52, co-authored 147 publications receiving 17600 citations. Previous affiliations of Dani Lischinski include Cornell University & University of Washington.
Papers
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Journal ArticleDOI
A Closed-Form Solution to Natural Image Matting
TL;DR: A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
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Colorization using optimization
TL;DR: This paper presents a simple colorization method that requires neither precise image segmentation, nor accurate region tracking, and demonstrates that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input.
Proceedings ArticleDOI
Gradient domain high dynamic range compression
TL;DR: The 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.
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Edge-preserving decompositions for multi-scale tone and detail manipulation
TL;DR: This paper advocates the use of an alternative edge-preserving smoothing operator, based on the weighted least squares optimization framework, which is particularly well suited for progressive coarsening of images and for multi-scale detail extraction.
Proceedings ArticleDOI
Joint bilateral upsampling
TL;DR: It is demonstrated that in cases, such as those above, the available high resolution input image may be leveraged as a prior in the context of a joint bilateral upsampling procedure to produce a better high resolution solution.