M
Matan Protter
Researcher at Technion – Israel Institute of Technology
Publications - 18
Citations - 4862
Matan Protter is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Motion estimation & Video denoising. The author has an hindex of 10, co-authored 18 publications receiving 3962 citations.
Papers
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Book ChapterDOI
On single image scale-up using sparse-representations
TL;DR: This paper deals with the single image scale-up problem using sparse-representation modeling, and assumes a local Sparse-Land model on image patches, serving as regularization, to recover an original image from its blurred and down-scaled noisy version.
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
Image Sequence Denoising via Sparse and Redundant Representations
Matan Protter,Michael Elad +1 more
TL;DR: This paper generalizes the above algorithm by offering several extensions: i) the atoms used are 3-D; ii) the dictionary is propagated from one frame to the next, reducing the number of required iterations; and iii) averaging is done on patches in both spatial and temporal neighboring locations.
Posted Content
Asymmetric Loss For Multi-Label Classification
Emanuel Ben-Baruch,Tal Ridnik,Nadav Zamir,Asaf Noy,Itamar Friedman,Matan Protter,Lihi Zelnik-Manor +6 more
TL;DR: This paper introduces a novel asymmetric loss ("ASL"), which enables to dynamically down-weights and hard-thresholds easy negative samples, while also discarding possibly mislabeled samples and demonstrating ASL applicability for other tasks, such as single-label classification and object detection.