scholarly journals Long-Distance Object Recognition With Image Super Resolution: A Comparative Study

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 13429-13438 ◽  
Author(s):  
Xiaomin Yang ◽  
Wei Wu ◽  
Kai Liu ◽  
Pyoung Won Kim ◽  
Arun Kumar Sangaiah ◽  
...  
Keyword(s):  
Object Recognition ◽  
Comparative Study ◽  
Super Resolution ◽  
Long Distance ◽  
Image Super Resolution

A comparative study of multi-scale image super-resolution techniques

2016 ◽  
Author(s):  
Michael Giansiracusa ◽  
Soundararajan Ezekiel ◽  
Joseph Raquepas ◽  
Erik Blasch ◽  
Millicent Thomas
Keyword(s):  
Comparative Study ◽  
Super Resolution ◽  
Multi Scale ◽  
Image Super Resolution

scholarly journals Super-Resolved Recognition of License Plate Characters

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2494
Author(s):  
Sung-Jin Lee ◽  
Seok Bong Yoo
Keyword(s):  
Object Recognition ◽  
Character Recognition ◽  
Recognition Accuracy ◽  
Super Resolution ◽  
Image Data ◽  
License Plate ◽  
Data Generation ◽  
Actual Object ◽  
Training Images ◽  
Image Super Resolution

Object detection and recognition are crucial in the field of computer vision and are an active area of research. However, in actual object recognition processes, recognition accuracy is often degraded due to resolution mismatches between training and test image data. To solve this problem, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique that improves object recognition accuracy. In detail, we collected a number of license plate training images through web-crawling and artificial data generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to image flips. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on representative test images and confirmed that the proposed super-resolution technique improves the accuracy of character recognition. For character recognition with the 4× magnification, the proposed method remarkably increased the mean average precision by 49.94% compared to the existing state-of-the-art method.

scholarly journals A Comparable Study of CNN-Based Single Image Super-Resolution for Space-Based Imaging Sensors

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3234 ◽  
Author(s):  
Haopeng Zhang ◽  
Pengrui Wang ◽  
Cong Zhang ◽  
Zhiguo Jiang
Keyword(s):  
Super Resolution ◽  
Image Data ◽  
Target Space ◽  
Single Image ◽  
Long Distance ◽  
Space Applications ◽  
Space Objects ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Imaging Sensors

In the case of space-based space surveillance (SBSS), images of the target space objects captured by space-based imaging sensors usually suffer from low spatial resolution due to the extremely long distance between the target and the imaging sensor. Image super-resolution is an effective data processing operation to get informative high resolution images. In this paper, we comparably study four recent popular models for single image super-resolution based on convolutional neural networks (CNNs) with the purpose of space applications. We specially fine-tune the super-resolution models designed for natural images using simulated images of space objects, and test the performance of different CNN-based models in different conditions that are mainly considered for SBSS. Experimental results show the advantages and drawbacks of these models, which could be helpful for the choice of proper CNN-based super-resolution method to deal with image data of space objects.

Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components

2013 ◽  
Vol E96.A (6) ◽  
pp. 1315-1322 ◽  
Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Frequency Components ◽  
Image Super Resolution

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution
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