Deep Convolutional Neural Network with Feature Fusion for Image Super-Resolution

2018 ◽  
pp. 289-298
Author(s):  
Furui Bai ◽  
Rui Wang ◽  
Xiaopeng Sun ◽  
Huxing Sun ◽  
Wen Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Super Resolution ◽  
Deep Convolutional Neural Network ◽  
Image Super Resolution

scholarly journals Hyperspectral image super-resolution using deep convolutional neural network

2017 ◽  
Vol 266 ◽  
pp. 29-41 ◽  
Author(s):  
Yunsong Li ◽  
Jing Hu ◽  
Xi Zhao ◽  
Weiying Xie ◽  
JiaoJiao Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Deep Convolutional Neural Network ◽  
Image Super Resolution

Radar Super Resolution Using a Deep Convolutional Neural Network

2020 ◽  
Vol 37 (12) ◽  
pp. 2197-2207
Author(s):  
Andrew Geiss ◽  
Joseph C. Hardin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Super Resolution ◽  
Deep Convolutional Neural Network ◽  
Perceptual Quality ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Pixel Scale ◽  
Made In

AbstractSuper resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics.

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