Super-resolution (SR) MR image reconstruction has shown to be a very promising direction to improve the spatial resolution of low-resolution (LR) MR images. In this paper, we presented a novel MR image SR method based on a dense convolutional neural network (DDSR), and its enhanced version...
In recent years, convolutional neural networks have achieved considerable success in different computer vision tasks, including image denoising. In this work, we present a residual dense neural network (RDUNet) for image denoising based on the densely connected hierarchical network. The encoding and de...
Hyperspectral Image Classification using Hybrid Deep Convolutional Neural Network The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computat...
Dense convolutional neural network architecture is trained on a large plant leaves image dataset from multiple countries. Six crops in 27 different categories are considered in the proposed work in laboratory and on-field conditions. Images have several inter-class and intra-class variations with ...
A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However,most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, the...
A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, ...
(A and C) to mark data helped train a neural network model to detect small, geometric objects within dense, low-quality plots. The purpose of the project was to recover the lost data in journal articles, but this type of object detection also has other applications such as image analyses,...
To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.This is a preview of subscription content, access via your ...
cascaded networkresidual learningdeep learningsingle image super-resolutionRecently, deep convolutional neural networks (CNNs) have achieved great success in single image super-resolution (SISR). Especially, dense skip connections and residual learning structures promote better performance. While most existing...
Javier Gurrola-Ramos,Oscar DalmauandTeresa E. Alarcón,"A Residual Dense U-Net Neural Network for Image Denoising", IEEE Access, vol. 9, pp. 31742-31754, 2021, doi:10.1109/ACCESS.2021.3061062. Citation If you use this paper work in your research or work, please cite our paper: ...