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Aseem Agarwala
Researcher at Adobe Systems
Publications - 78
Citations - 10423
Aseem Agarwala is an academic researcher from Adobe Systems. The author has contributed to research in topics: Video tracking & Pixel. The author has an hindex of 40, co-authored 77 publications receiving 9428 citations. Previous affiliations of Aseem Agarwala include Microsoft & University of Washington.
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Journal ArticleDOI
High-quality motion deblurring from a single image
Qi Shan,Jiaya Jia,Aseem Agarwala +2 more
TL;DR: A new algorithm for removing motion blur from a single image is presented using a unified probabilistic model of both blur kernel estimation and unblurred image restoration and is able to produce high quality deblurred results in low computation time.
Journal ArticleDOI
Interactive digital photomontage
Aseem Agarwala,Mira Dontcheva,Maneesh Agrawala,Steven M. Drucker,Alex Colburn,Brian Curless,David Salesin,Michael Cohen +7 more
TL;DR: The framework makes use of two techniques primarily: graph-cut optimization, to choose good seams within the constituent images so that they can be combined as seamlessly as possible; and gradient-domain fusion, a process based on Poisson equations, to further reduce any remaining visible artifacts in the composite.
Journal ArticleDOI
A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Richard Szeliski,Ramin Zabih,Daniel Scharstein,Olga Veksler,Vladimir Kolmogorov,Aseem Agarwala,Marshall F. Tappen,Carsten Rother +7 more
TL;DR: A set of energy minimization benchmarks are described and used to compare the solution quality and runtime of several common energy minimizations algorithms and a general-purpose software interface is provided that allows vision researchers to easily switch between optimization methods.
Proceedings ArticleDOI
Video Frame Synthesis Using Deep Voxel Flow
TL;DR: Deep voxel flow as mentioned in this paper combines the advantages of optical flow and neural network-based methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which can be applied at any video resolution.
Journal ArticleDOI
Content-preserving warps for 3D video stabilization
TL;DR: A technique that transforms a video from a hand-held video camera so that it appears as if it were taken with a directed camera motion, and develops algorithms that can effectively recreate dynamic scenes from a single source video.