Parameter calibration of a novel super-resolution model for a compressed-sensing measurement setup

2011 ◽  
Author(s):  
Torsten Edeler ◽  
Kevin Ohliger ◽  
Stephan Hussmann ◽  
Alfred Mertins
Keyword(s):  
Compressed Sensing ◽  
Super Resolution ◽  
Parameter Calibration ◽  
Resolution Model

scholarly journals Bistatic ISAR Imaging with a V-FM Waveform Based on a Dual-Channel-Coupled 2D-CS Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3082 ◽  
Author(s):  
Jiyuan Chen ◽  
Xiaoyi Pan ◽  
Letao Xu ◽  
Wei Wang
Keyword(s):  
Compressed Sensing ◽  
Optimization Problem ◽  
Super Resolution ◽  
Space Distribution ◽  
Signal Model ◽  
Low Snr ◽  
Dual Channel ◽  
Isar Imaging ◽  
Radar Echo ◽  
Resolution Model

Due to the sparsity of the space distribution of point scatterers and radar echo data, the theory of Compressed Sensing (CS) has been successfully applied in Inverse Synthetic Aperture Radar (ISAR) imaging, which can recover an unknown sparse signal from a limited number of measurements by solving a sparsity-constrained optimization problem. In this paper, since the V style modulation(V-FM) signal can mitigate the ambiguity apparent in range and velocity, the dual-channel, two-dimension, compressed-sensing (2D-CS) algorithm is proposed for Bistatic ISAR (Bi-ISAR) imaging, which directly deals with the 2D signal model for image reconstruction based on solving a nonconvex optimization problem. The coupled 2D super-resolution model of the target’s echoes is firstly established; then, the 2D-SL0 algorithm is applied in each channel with different dictionaries, and the final image is obtained by synthesizing the two channels. Experiments are used to test the robustness of the Bi-ISAR imaging framework with the two-dimensional CS method. The results show that the framework is capable accurately reconstructing the Bi-ISAR image within the conditions of low SNR and low measured data.

scholarly journals Super-Resolution Model for a Compressed-Sensing Measurement Setup

2012 ◽  
Vol 61 (5) ◽  
pp. 1140-1148 ◽  
Author(s):  
Torsten Edeler ◽  
Kevin Ohliger ◽  
Stephan Hussmann ◽  
Alfred Mertins
Keyword(s):  
Compressed Sensing ◽  
Super Resolution ◽  
Resolution Model

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

Super-resolution compressed sensing imaging algorithm based on sub-pixel shift

2018 ◽  
Vol 22 (S4) ◽  
pp. 8407-8413 ◽  
Author(s):  
Bing Xu ◽  
Xiaoping Zhang ◽  
Xianjun Wu
Keyword(s):  
Compressed Sensing ◽  
Super Resolution ◽  
Imaging Algorithm

scholarly journals A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI

2015 ◽  
pp. 57-68 ◽  
Author(s):  
Lipeng Ning ◽  
Kawin Setsompop ◽  
Oleg Michailovich ◽  
Nikos Makris ◽  
Carl-Fredrik Westin ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Diffusion Mri ◽  
Super Resolution
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