S
Stamatios Lefkimmiatis
Researcher at Skolkovo Institute of Science and Technology
Publications - 43
Citations - 2125
Stamatios Lefkimmiatis is an academic researcher from Skolkovo Institute of Science and Technology. The author has contributed to research in topics: Deblurring & Deep learning. The author has an hindex of 17, co-authored 42 publications receiving 1727 citations. Previous affiliations of Stamatios Lefkimmiatis include University of California, Los Angeles & École Normale Supérieure.
Papers
More filters
Proceedings ArticleDOI
Non-local Color Image Denoising with Convolutional Neural Networks
TL;DR: In this article, the authors proposed a non-local image denoising network based on variational methods that exploit the inherent nonlocal self-similarity property of natural images and showed that the proposed network achieved state-of-the-art performance on the Berkeley segmentation dataset.
Proceedings ArticleDOI
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising
TL;DR: A novel network architecture for learning discriminative image models that are employed to efficiently tackle the problem of grayscale and color image denoising is designed and two different variants are introduced, which achieve excellent results under additive white Gaussian noise.
Journal ArticleDOI
Hessian-Based Norm Regularization for Image Restoration With Biomedical Applications
TL;DR: It is shown that the resulting regularizers retain some of the most favorable properties of TV, i.e., convexity, homogeneity, rotation, and translation invariance, while dealing effectively with the staircase effect.
Posted Content
Non-Local Color Image Denoising with Convolutional Neural Networks
TL;DR: In this article, the authors proposed a non-local image denoising network based on variational methods that exploit the inherent nonlocal self-similarity property of natural images and showed that the proposed network achieved state-of-the-art performance on the Berkeley segmentation dataset.
Journal ArticleDOI
Hessian Schatten-Norm Regularization for Linear Inverse Problems
TL;DR: A novel family of invariant, convex, and non-quadratic functionals is introduced that is employed to derive regularized solutions of ill-posed linear inverse imaging problems and is based on a primal-dual formulation.