H
Hiroyuki Takeda
Researcher at Sony Broadcast & Professional Research Laboratories
Publications - 30
Citations - 3192
Hiroyuki Takeda is an academic researcher from Sony Broadcast & Professional Research Laboratories. The author has contributed to research in topics: Kernel regression & Pixel. The author has an hindex of 12, co-authored 30 publications receiving 3015 citations. Previous affiliations of Hiroyuki Takeda include Sharp & University of Michigan.
Papers
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Journal ArticleDOI
Kernel Regression for Image Processing and Reconstruction
TL;DR: This paper adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more and establishes key relationships with some popular existing methods and shows how several of these algorithms are special cases of the proposed framework.
Journal ArticleDOI
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction
TL;DR: This paper shows how this denoising method is generalized to become a relatively simple super-resolution algorithm with no explicit motion estimation, and results show that the proposed method is very successful in providing super- resolution on general sequences.
Journal ArticleDOI
Super-Resolution Without Explicit Subpixel Motion Estimation
TL;DR: This paper introduces a novel framework for adaptive enhancement and spatiotemporal upscaling of videos containing complex activities without explicit need for accurate motion estimation based on multidimensional kernel regression, which significantly widens the applicability of super-resolution methods to a broad variety of video sequences containing complex motions.
Journal ArticleDOI
Deblurring Using Regularized Locally Adaptive Kernel Regression
TL;DR: The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature and achieves an optimal solution jointly denoises and deblurs images.
Proceedings ArticleDOI
Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data
TL;DR: A class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected samples are introduced.