Accurate RNA splicing is essential for gene expression and human health, yet predicting how DNA sequence variations affect ...

Abstract: Deep neural networks have achieved remarkable success in single image super-resolution (SISR). However, in most cases, image SR with different scale factors is considered as different tasks ...

This is a tensorflow implementation of "Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network", a deep learning based Single-Image Super-Resolution (SISR) ...

The advancement of tactile sensing in robotics and prosthetics is constrained by the trade-off between spatial and temporal resolution in artificial tactile sensors. To address this limitation, we ...

Nvidia’s RTX series include some of the best graphics cards on the market and are known for two flagship features: real-time ray tracing and Deep Learning Super Sampling (DLSS). While ray tracing is ...

Abstract: The trade-off between spatial and spectral resolution is one of the fundamental issues in hyperspectral images (HSI). Given the challenges of directly acquiring high-resolution hyperspectral ...

Imagine going to your local hardware store and seeing a new kind of hammer on the shelf. You’ve heard about this hammer: It pounds faster and more accurately than others, and in the last few years ...

This is an open source project from original of this: SRCNN_Cpp is a C++ Implementation of Image Super-Resolution using SRCNN which is proposed by Chao Dong in 2014. If you want to find the details of ...