Abstract: We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find ...

Abstract: We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we ...

AIIA Lab, Harbin Institute of Technology. This repository is the official PyTorch implementation of "Fully 1×1 Convolutional Network for Lightweight Image Super-Resolution". If our work helps your ...

This repository contains an implementation of SynthSR, a convolutional neural network that turns a clinical MRI scan (or even CT scan!) of any orientation, resolution and contrast into 1 mm isotropic ...

Background Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk ...

A new study explores deep learning for image-based defect detection during 3D printing, looking to catch bad builds.

Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...

A difficulty-graded mouse brain dataset pairs 3D microscopy images with verified neuron reconstructions to support AI-driven ...

Using AI and Australia's supercomputing infrastructure, researchers mapped 94 million craters on Mars, transforming planetary ...

The spatial organization of chromatophore-muscle innervation by motoneurons enables the generation of chromatophore-shaped noise, virtual or composite chromatophores, and shape elements such as lines ...

Bioinformatics is a field of study that uses computation to extract knowledge from biological data. It includes the collection, storage, retrieval, manipulation and modelling of data for analysis, ...