Study on Application of Super-Resolution Image Reconstruction Method Used in Video Image Processing

2012 ◽  
Vol 476-478 ◽  
pp. 1142-1145
Author(s):  
Jing Jia Qi ◽  
Chuan Jun Guo ◽  
Yang Nan
Keyword(s):  
Image Reconstruction ◽  
Performance Improvement ◽  
Super Resolution ◽  
Imaging Systems ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Resolution Image ◽  
Image Reconstruction Method ◽  
Significant Performance ◽  
Resolution Imaging

Super resolution image reconstruction is a computational process of using multiple low-resolution observations to reconstruct a higher resolution image, which differs from improvement of optical devices. With magnification diversity among those low-resolution imagers, significant performance improvement, compared to traditional methods, is demonstrated. Results include fidelity metrics and simulated reconstructions. Performance improvement of super-resolution imaging systems with magnification diversity is studied in this paper.

scholarly journals Fast Learning-based Super-resolution Image Reconstruction Method Utilizing TV Regularization

2011 ◽  
Vol 65 (11) ◽  
pp. 1621-1627
Author(s):  
Tomio Goto ◽  
Yasutaka Sakuta ◽  
Yuuta Kawamoto ◽  
Satoshi Kiriyama ◽  
Shotaro Suzuki ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Resolution Image ◽  
Fast Learning ◽  
Tv Regularization ◽  
Image Reconstruction Method

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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