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Dikpal Reddy
Researcher at Nvidia
Publications - 23
Citations - 1565
Dikpal Reddy is an academic researcher from Nvidia. The author has contributed to research in topics: Image resolution & Compressed sensing. The author has an hindex of 15, co-authored 23 publications receiving 1403 citations. Previous affiliations of Dikpal Reddy include Mitsubishi Electric Research Laboratories & University of California, Berkeley.
Papers
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Book ChapterDOI
Compressive Sensing for Background Subtraction
Volkan Cevher,Aswin C. Sankaranarayanan,Marco F. Duarte,Dikpal Reddy,Richard G. Baraniuk,Rama Chellappa +5 more
TL;DR: A method to directly recover background subtracted images using CS and its applications in some communication constrained multi-camera computer vision problems is described and its approach is suitable for image coding in communication constrained problems.
Journal ArticleDOI
FlexISP: a flexible camera image processing framework
Felix Heide,Markus Steinberger,Yun-Ta Tsai,Mushfiqur Rouf,Dawid Pająk,Dikpal Reddy,Orazio Gallo,Jing Liu,Wolfgang Heidrich,Karen Egiazarian,Jan Kautz,Kari Pulli +11 more
TL;DR: This work proposes an end-to-end system that is aware of the camera and image model, enforces natural-image priors, while jointly accounting for common image processing steps like demosaicking, denoising, deconvolution, and so forth, all directly in a given output representation.
Proceedings ArticleDOI
P2C2: Programmable pixel compressive camera for high speed imaging
TL;DR: By modeling such spatio-temporal redundancies in a video volume, one can faithfully recover the underlying high-speed video frames from the observed low speed coded video by proposing a reconstruction algorithm that uses the data from P2C2 along with additional priors about videos to perform temporal super-resolution.
Journal ArticleDOI
Coded Strobing Photography: Compressive Sensing of High Speed Periodic Videos
TL;DR: The problem of sub-Nyquist sampling of periodic signals and designs to capture and reconstruct such signals are addressed and the key result is that for such signals, the Nyquist rate constraint can be imposed on the strobe rate rather than the sensor rate.
Proceedings ArticleDOI
Robust Model-Based 3D Head Pose Estimation
TL;DR: This work performs pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D registration and a 2D overlap term.