Results 41 to 50 of about 53,402 (265)

Denoising an Image by Denoising Its Components in a Moving Frame [PDF]

, 2014
This work was supported by European Research Council, Starting Grant ref. 306337, and by Spanish grants AACC, ref. TIN2011-15954-E, and Plan Na- cional, ref. TIN2012-38112. S. Levine acknowledges partial support by NSF-DMS #0915219.
Gabriela Ghimpeteanu, Thomas Batard, Marcelo Bertalmío, Stacey Levine   +3 more
openaire   +2 more sources

Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors, 2021
Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra.
Sinead Barton, Salaheddin Alakkari, Kevin O’Dwyer, Tomas Ward, Bryan Hennelly   +4 more
doaj   +1 more source

Spatial and single‐nuclei transcriptomics reveals idiosyncratic and generic patterns in papillary and anaplastic thyroid cancers

Molecular Oncology, EarlyView.
Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur, Joel Rodrigues Vitória, Manuel Saiselet, Ligia Craciun, Denis Larsimont, Anne Lefort, Frédérick Libert, Carine Maenhaut, Sabine Costagliola, Maxime Tarabichi, Mirian Romitti, Vincent Detours   +11 more
wiley   +1 more source

Overview of Research on Digital Image Denoising Methods

Sensors
During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like ...
Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu   +3 more
doaj   +1 more source

Analysing the significance of small conformational changes and low occupancy states in serial crystallographic data

FEBS Open Bio, EarlyView.
This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill, Yelyzaveta Pulnova, Elke De Zitter, Helen M. Ginn, Briony A. Yorke   +4 more
wiley   +1 more source

Coupling Denoising to Detection for SAR Imagery

Applied Sciences, 2021
Detecting objects in synthetic aperture radar (SAR) imagery has received much attention in recent years since SAR can operate in all-weather and day-and-night conditions. Due to the prosperity and development of convolutional neural networks (CNNs), many
Sujin Shin, Youngjung Kim, Insu Hwang, Junhee Kim, Sungho Kim   +4 more
doaj   +1 more source

PET image denoising based on denoising diffusion probabilistic model

European Journal of Nuclear Medicine and Molecular Imaging, 2023
8 ...
Kuang Gong, Keith Johnson, Georges El Fakhri, Quanzheng Li, Tinsu Pan   +4 more
openaire   +3 more sources

Evaluating the effect of γ‐oryzanol on MASLD pathology using a medaka fish model

FEBS Open Bio, EarlyView.
This study explores a liver disease called MASLD, which is increasing worldwide and can lead to serious damage. Researchers used medaka fish instead of rodents to test a food compound, γ‐oryzanol. Fish fed this compound had less liver fat and healthier gut bacteria.
Yukako Ito, Haduki Asano, Ayano Ueki, Joe Sakamoto, Yasuhiro Kamei, Hayato Tokumoto, Ayaka Yazawa, Shigeki Kamitani   +7 more
wiley   +1 more source

Variational Low-Rank Matrix Factorization with Multi-Patch Collaborative Learning for Hyperspectral Imagery Mixed Denoising

Remote Sensing, 2021
In this study, multi-patch collaborative learning is introduced into variational low-rank matrix factorization to suppress mixed noise in hyperspectral images (HSIs).
Shuai Liu, Jie Feng, Zhiqiang Tian
doaj   +1 more source

From denoising diffusions to denoising Markov models

Journal of the Royal Statistical Society Series B: Statistical Methodology
Abstract Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution and then learning to reverse this noising process to obtain synthetic datapoints.
Joe Benton, Yuyang Shi 0002, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet   +4 more
openaire   +4 more sources