This paper proposes a joint optimization method for the imaging algorithm and sampling scheme of sparse spotlight synthetic aperture radar (SAR) imaging based on deep convolutional neural networks. Traditional compressed sensing-based sparse SAR imaging has been widely studied. Deep learning and sparse unfolding networks have been introduced into sparse SAR imaging, but most current works focus only on the imaging stage and simply adopt the conventional uniform or random downsampling scheme. Considering that the imaging quality also depends on the sampling pattern besides the imaging algorithm, this paper introduces a learning-based strategy to jointly optimize the sampling scheme and the imaging network parameters of the reconstruction module. In a deep learning-based image reconstruction scheme, joint and continuous optimization of the sampling patterns and convolutional neural network parameters is achieved to improve the image quality. Simulation results based on real SAR image data set illustrate the effectiveness and superiority of the proposed framework.
Data availability statement:The data that support the findings of this study are available from US Air Force at https://www.sdms.afrl.af.mil/ index.php?collection=mstar. The data set is open for public use.
In this paper, we propose a method for cracking the key parameters of an electro-optic self-feedback temporal optical phase encryption system and experimentally demonstrate the feasibility of the scheme. By scanning a tunable dispersion compensation (TDC) module at the receiver, the time delay signature (TDS) of an encrypted signal can be exposed, making it possible to extract other key parameters of the system and reconstruct a decryption setup. The TDS characteristics for three typical modulation formats are investigated, revealing that while such an encryption system is secure against power detection attack, there is a risk of TDS leakage. The findings can guide the design of advanced optical encryption schemes with TDS suppression for security enhancement.
This paper proposes a joint optimization method for the imaging
algorithm and sampling scheme of sparse spotlight syhthetic aperture
radar (SAR) imaging based on deep convolutional neural networks.
Traditional compressed sensing (CS) based sparse SAR imaging has been
widely studied. Deep learning and sparse unfolding networks have been
introduced into sparse SAR imaging, but most current works focus only on
the imaging stage and simply adopt the conventional uniform or random
down-sampling scheme. Considering that the imaging quality also depends
on the sampling pattern besides the imaging algorithm, this paper
introduces a learning-based strategy to jointly optimize the sampling
scheme and the imaging network parameters of the reconstruction module.
In a deep learning-based image reconstruction scheme, joint and
continuous optimization of the sampling patterns and convolutional
neural network parameters is achieved to improve the image quality.
Simulation results based on real SAR image dataset illustrate the
effectiveness and superiority of the proposed framework.
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