SAR raw data missing occurs when radar is interrupted for various reasons. In order to deal with this problem, a method is proposed to focus the missing SAR raw data via stage-wise orthogonal matching pursuit (stomp)....
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SAR raw data missing occurs when radar is interrupted for various reasons. In order to deal with this problem, a method is proposed to focus the missing SAR raw data via stage-wise orthogonal matching pursuit (stomp). A reference function in time domain is designed for the missing raw data. After matched with the reference function, the energy of missing raw data is concentrated in two-dimensional frequency domain. Then, stomp algorithm is available to recover the matched raw data in two-dimensional frequency domain. The recovered raw data is available to be processed with traditional SAR imaging algorithms. The Omega-K algorithm is chosen to deal with the recovered raw data in experiments. Point target and area target simulations are carried out to validate the effectiveness of the proposed method for azimuth missing SAR raw data. The data missing rate is 50% in both point and area target simulation. The resolution of point targets can reach 0.3m in both range and azimuth direction.
Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinea...
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ISBN:
(纸本)9781424441242
Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm selection in the practical MRI application. A systematic and comparative study of those commonly used algorithms is therefore essential for the implementation of CS in MRI. In this work, three typical algorithms, namely, the Gradient Projection For Sparse Reconstruction (GPSR) algorithm, Interior-point algorithm (l(1)_ls), and the Stagewise Orthogonal Matching Pursuit (stomp) algorithm are compared and investigated in three different imaging scenarios, brain, angiogram and phantom imaging. The algorithms' performances are characterized in terms of image quality and reconstruction speed. The theoretical results show that the performance of the CS algorithms is case sensitive;overall, the stomp algorithm offers the best solution in imaging quality, while the GPSR algorithm is the most efficient one among the three methods. In the next step, the algorithm performances and characteristics will be experimentally explored. It is hoped that this research will further support the applications of CS in MRI.
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