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Anat Levin
Researcher at Technion – Israel Institute of Technology
Publications - 107
Citations - 14409
Anat Levin is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Speckle pattern & Scattering. The author has an hindex of 42, co-authored 91 publications receiving 12993 citations. Previous affiliations of Anat Levin include Stanford University & Hebrew University of Jerusalem.
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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.
Journal ArticleDOI
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
Image and depth from a conventional camera with a coded aperture
TL;DR: A simple modification to a conventional camera is proposed to insert a patterned occluder within the aperture of the camera lens, creating a coded aperture, and introduces a criterion for depth discriminability which is used to design the preferred aperture pattern.
Proceedings ArticleDOI
Understanding and evaluating blind deconvolution algorithms
TL;DR: The previously reported failure of the naive MAP approach is explained by demonstrating that it mostly favors no-blur explanations and it is shown that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur.
Proceedings 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.