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pro vyhledávání: '"Mousavi, Hojjat"'
Autor:
Entezari, Ehsan, Velázquez, Jorge Luis González, López, Diego Rivas, Zúñiga, Manuel Alejandro Beltrán, Mousavi, Hojjat, Davani, Reza Khatib Zadeh, Szpunar, Jerzy
Publikováno v:
In Engineering Failure Analysis December 2023 154
Deep learning methods, in particular, trained Convolutional Neural Networks (CNN) have recently been shown to produce compelling results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the Low Resolution (LR) image to its
Externí odkaz:
http://arxiv.org/abs/1904.10082
Autor:
Mousavi, Hojjat Seyed
Parsimony in signal representation is a topic of active research. Sparse signal processing and representation is the outcome of this line of research which has many applications in information processing and has shown significant improvement in real-
Externí odkaz:
http://arxiv.org/abs/1805.04828
Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR)
Externí odkaz:
http://arxiv.org/abs/1802.02721
Deep learning methods, in particular trained Convolutional Neural Networks (CNNs) have recently been shown to produce compelling state-of-the-art results for single image Super-Resolution (SR). Invariably, a CNN is learned to map the low resolution (
Externí odkaz:
http://arxiv.org/abs/1802.02018
Autor:
Mousavi, Hojjat S., Monga, Vishal
Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coeffi
Externí odkaz:
http://arxiv.org/abs/1610.01066
Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification.
Externí odkaz:
http://arxiv.org/abs/1610.08495
In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an aut
Externí odkaz:
http://arxiv.org/abs/1506.05032
In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike a
Externí odkaz:
http://arxiv.org/abs/1502.04726
DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification
In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structure. In this paper, we propose an auto
Externí odkaz:
http://arxiv.org/abs/1502.01032