Computational microscopy is vital in biomedicine and materials science. Traditional methods struggle with optical aberrations ...
Advancements in medical imaging and computational methodologies have significantly transformed the field of ophthalmology, allowing for unmatched precision ...
Abstract: Existing single-image denoising methods suffer from several issues, including reliance on noise distribution models, inefficient network training, and the loss of useful information during ...
Abstract: This paper introduces a novel hyperspectral image (HSI) denoising method based on Hyper-Laplacian Total Variation with Spectral Gradient (HLTVSG). Our research identifies that, compared to ...
Study Finds on MSN9d
Scientists observe the start of life: Quantum cameras capture embryo development with unprecedented detailNew camera techniques transform biological imaging In a nutshell Optimizing camera settings can provide better images ...
In the fast-changing field of artificial intelligence (AI), the importance of high-quality data can’t be overstated.
A deep-learning algorithm has enabled ultra-low dose CT scans to diagnose pneumonia with only 2% of the radiation of standard ...
title = {Blind2Unblind: Self-Supervised Image Denoising With Visible Blind Spots}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June ...
This reduction in image quality can affect the accuracy of diagnosis. Therefore, Dr. Klug and colleagues sought to test the ...
In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT ...
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