British Journal of Ophthalmology

Quantitative reference map of retinopathy of prematurity improves diagnostic reliability and treatment decisions

Background/Aims Retinopathy of prematurity (ROP) zone classification guides disease severity assessment and Early Treatment ...

WiMi Releases Resource-Efficient Quantum Convolutional Neural Network Based on QRAM, Accelerating the Practical Implementation of Large-Scale Image Classification Applications

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the release of its latest independently developed ...
pv magazine International

Deep learning model improves PV segmentation in remote sensing images

Scientists have developed a deep learning model that improves PV panel segmentation in remote sensing images, by combining ...
Science Daily

Scientists built a camera that can track invisible particles in 3D

A new particle detector called PLATON could replace millions of tiny detector components with a single block of ...
IEEE

High-Resolution Image Reconstruction Using Convolutional Neural Network-Based Deep Learning Models

Abstract: This paper presents an optimized lightweight Super-Resolution Convolutional Neural Network (SRCNN) capable of reconstructing high-quality images with strong fidelity. The proposed framework ...
The New York Times

How Trump Is Using Fake Imagery to Attack Enemies and Rouse Supporters

The era of A.I. propaganda is here — and President Trump is an enthusiastic participant. After nationwide protests this weekend against Mr. Trump’s administration, the president posted an ...
PNAS

Descattering and image restoration with a transformer-based neural network in deep tissue imaging

The significant contributions of this work are threefold. First, it leverages deep learning to extend in vivo imaging depth of two-photon excitation fluorescence microscopy, far beyond the depths ...
IEEE

Image Super-Resolution Using Deep Convolutional Networks

Abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented ...