A Review on Iterative Shrinkage Approach to Deconvolution Problem

2021 ◽  
Author(s):  
Avinash Kumar ◽  
Sujit Kumar Sahoo
Keyword(s):  
Iterative Shrinkage ◽  
Deconvolution Problem

scholarly journals Deconvolution problem of deeply virtual Compton scattering

2021 ◽  
Vol 103 (11) ◽  
Author(s):  
V. Bertone ◽  
H. Dutrieux ◽  
C. Mezrag ◽  
H. Moutarde ◽  
P. Sznajder
Keyword(s):  
Compton Scattering ◽  
Deeply Virtual Compton Scattering ◽  
Virtual Compton Scattering ◽  
Deconvolution Problem

scholarly journals An Elementary Approximation of Dwell Time Algorithm for Ultra-Precision Computer-Controlled Optical Surfacing

Micromachines ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 471
Author(s):  
Yajun Wang ◽  
Yunfei Zhang ◽  
Renke Kang ◽  
Fang Ji
Keyword(s):  
Dwell Time ◽  
Convergence Rates ◽  
Rotational Symmetry ◽  
Time Algorithm ◽  
Two Dimensions ◽  
Efficient Computation ◽  
Deconvolution Problem ◽  
Ultra Precision ◽  
Computer Controlled Optical Surfacing ◽  
Computer Controlled

The dwell time algorithm is one of the key technologies that determines the accuracy of a workpiece in the field of ultra-precision computer-controlled optical surfacing. Existing algorithms mainly consider meticulous mathematics theory and high convergence rates, making the computation process more uneven, and the flatness cannot be further improved. In this paper, a reasonable elementary approximation algorithm of dwell time is proposed on the basis of the theoretical requirement of a removal function in the subaperture polishing and single-peak rotational symmetry character of its practical distribution. Then, the algorithm is well discussed with theoretical analysis and numerical simulation in cases of one-dimension and two-dimensions. In contrast to conventional dwell time algorithms, this proposed algorithm transforms superposition and coupling features of the deconvolution problem into an elementary approximation issue of function value. Compared with the conventional methods, it has obvious advantages for improving calculation efficiency and flatness, and is of great significance for the efficient computation of large-aperture optical polishing. The flatness of φ150 mm and φ100 mm workpieces have achieved PVr150 = 0.028 λ and PVcr100 = 0.014 λ respectively.

Accelerated 3D blind separation of convolved mixtures based on the fast iterative shrinkage thresholding algorithm for adaptive multiple subtraction

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. V99-V113 ◽  
Author(s):  
Zhong-Xiao Li ◽  
Zhen-Chun Li
Keyword(s):  
Field Data ◽  
Optimization Problem ◽  
Computational Cost ◽  
Computation Time ◽  
Blind Separation ◽  
L1 Norm ◽  
Norm Minimization ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

After multiple prediction, adaptive multiple subtraction is essential for the success of multiple removal. The 3D blind separation of convolved mixtures (3D BSCM) method, which is effective in conducting adaptive multiple subtraction, needs to solve an optimization problem containing L1-norm minimization constraints on primaries by the iterative reweighted least-squares (IRLS) algorithm. The 3D BSCM method can better separate primaries and multiples than the 1D/2D BSCM method and the method with energy minimization constraints on primaries. However, the 3D BSCM method has high computational cost because the IRLS algorithm achieves nonquadratic optimization with an LS optimization problem solved in each iteration. In general, it is good to have a faster 3D BSCM method. To improve the adaptability of field data processing, the fast iterative shrinkage thresholding algorithm (FISTA) is introduced into the 3D BSCM method. The proximity operator of FISTA can solve the L1-norm minimization problem efficiently. We demonstrate that our FISTA-based 3D BSCM method achieves similar accuracy of estimating primaries as that of the reference IRLS-based 3D BSCM method. Furthermore, our FISTA-based 3D BSCM method reduces computation time by approximately 60% compared with the reference IRLS-based 3D BSCM method in the synthetic and field data examples.

Image Reconstruction Based on Deep Iterative Shrinkage Network

2021 ◽  
Author(s):  
Jiahang Li ◽  
Qi Zhu ◽  
Yuezhou Wu ◽  
Xuyang Gao
Keyword(s):  
Image Reconstruction ◽  
Iterative Shrinkage

scholarly journals Frequency-Diverse Holographic Metasurface Antenna for Near-Field Microwave Computational Imaging

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiaqi Han ◽  
Long Li ◽  
Shuncheng Tian ◽  
Xiangjin Ma ◽  
Qiang Feng ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Imaging System ◽  
Near Field ◽  
Horn Antenna ◽  
Computational Imaging ◽  
Simulation Studies ◽  
Field Patterns ◽  
Full Wave ◽  
Imaging Reconstruction ◽  
Iterative Shrinkage

This article presents a holographic metasurface antenna with stochastically distributed surface impedance, which produces randomly frequency-diverse radiation patterns. Low mutual coherence electric field patterns generated by the holographic metasurface antenna can cover the K-band from 18 to 26 GHz with 0.1 GHz intervals. By utilizing the frequency-diverse holographic metasurface (FDHM) antenna, we build a near-field microwave computational imaging system based on reflected signals in the frequency domain. A standard horn antenna is adopted to acquire frequency domain signals radiated from the proposed FDHM antenna. A detail imaging restoration process is presented, and the desired targets are correctly reconstructed using the 81 frequency-diverse patterns through full-wave simulation studies. Compressed sensing technique and iterative shrinkage/thresholding algorithms are applied for the imaging reconstruction. The achieved compressive ratio of this computational imaging system on the physical layer is 30:1.

scholarly journals Fast Algorithms for the Anisotropic LLT Model in Image Denoising

2011 ◽  
Vol 1 (3) ◽  
pp. 264-283 ◽  
Author(s):  
Zhi-Feng Pang ◽  
Li-Lian Wang ◽  
Yu-Fei Yang
Keyword(s):  
Convergence Rate ◽  
Image Denoising ◽  
Projection Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Minimization Problems ◽  
Iterative Shrinkage ◽  
Speed Up ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

AbstractIn this paper, we propose a new projection method for solving a general minimization problems with twoL1-regularization terms for image denoising. It is related to the split Bregman method, but it avoids solving PDEs in the iteration. We employ the fast iterative shrinkage-thresholding algorithm (FISTA) to speed up the proposed method to a convergence rateO(k−2). We also show the convergence of the algorithms. Finally, we apply the methods to the anisotropic Lysaker, Lundervold and Tai (LLT) model and demonstrate their efficiency.

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