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Ramon Manzorro

Researcher at Arizona State University

Publications -  23
Citations -  225

Ramon Manzorro is an academic researcher from Arizona State University. The author has contributed to research in topics: Convolutional neural network & Artificial neural network. The author has an hindex of 4, co-authored 16 publications receiving 93 citations. Previous affiliations of Ramon Manzorro include University of Cádiz.

Papers
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Journal ArticleDOI

Structural modulation and direct measurement of subnanometric bimetallic PtSn clusters confined in zeolites

Lichen Liu, +10 more
TL;DR: In this paper, the authors used the Microscopy Service of the UPV for the TEM and STEM measurements in the CLAESS beamline of the ALBA synchrotron.
Posted Content

Unsupervised Deep Video Denoising.

Dev Yashpal Sheth, +7 more
- 30 Nov 2020 - 
TL;DR: An Unsupervised Deep Video Denoiser (UDVD1), a CNN architecture designed to be trained exclusively with noisy data, is proposed and the performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video.
Journal ArticleDOI

CeO2-modified Au/TiO2 catalysts with outstanding stability under harsh CO oxidation conditions

E. del Río, +6 more
- 15 Nov 2016 - 
TL;DR: The 1.5% Au/TiO2 World Gold Council (WGC) was modified by depositing on its surface a 5.4% CeO2 loading by incipient wetness impregnation as discussed by the authors.
Journal Article

Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy

Sreyas Mohan, +8 more
- 04 May 2021 - 
TL;DR: A simulation-based denoising (SBD) framework, in which CNNs are trained on simulated images, which outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data.
Journal ArticleDOI

Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise

Joshua L. Vincent, +8 more
- 16 Sep 2021 - 
TL;DR: An approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image is developed.