Comparison of different lithographic source optimization methods based on compressive sensing

2020 ◽  
Author(s):  
Zhiqiang Wang ◽  
Xu Ma ◽  
Rui Chen ◽  
Gonzalo Arce ◽  
Lisong Dong ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Optimization Methods

scholarly journals Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging

2020 ◽  
Vol 10 (9) ◽  
pp. 3288 ◽  
Author(s):  
Ziran Wei ◽  
Jianlin Zhang ◽  
Zhiyong Xu ◽  
Yong Liu
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Imaging System ◽  
Optimization Methods ◽  
Superior Performance ◽  
Gram Matrix ◽  
Constraint Matrix ◽  
Single Pixel ◽  
Sensing Matrices

According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot meet the demands of further engineering applications. In order to improve the imaging accuracy of our single-pixel camera, some optimization methods of key technologies in compressive sensing are proposed in this paper. First, in terms of sparse signal decomposition, based on traditional discrete wavelet transform and the characteristics of coefficients distribution in wavelet domain, a constraint condition of the exponential decay is proposed and a corresponding constraint matrix is designed to optimize the original wavelet decomposition basis. Second, for the construction of deterministic binary sensing matrices in the single-pixel camera, on the basis of a Gram matrix, a new algorithm model and a new method of initializing a compressed sensing measurement matrix are proposed to optimize the traditional binary sensing matrices via mutual coherence minimization. The gradient projection-based algorithm is used to solve the new mathematical model and train deterministic binary sensing measurement matrices with better performance. Third, the proposed optimization methods are applied to our single-pixel imaging system for optimizing the existing imaging methods. Compared with the conventional methods of single-pixel imaging, the accuracy of image reconstruction and the quality of single-pixel imaging have been significantly improved by our methods. The superior performance of our proposed methods has been fully tested and the effectiveness has also been demonstrated by numerical simulation experiments and practical imaging experiments.

scholarly journals Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

2020 ◽  
Author(s):  
Elin Farnell ◽  
Henry Kvinge ◽  
Julia R. Dupuis ◽  
Michael J. Kirby ◽  
Chris Peterson ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Total Variation ◽  
Optimization Methods ◽  
Chemical Detection ◽  
L1 Regularization

scholarly journals A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

2019 ◽  
Vol 33 ◽  
pp. 4368-4375 ◽  
Author(s):  
Risheng Liu ◽  
Yuxi Zhang ◽  
Shichao Cheng ◽  
Xin Fan ◽  
Zhongxuan Luo
Keyword(s):  
Compressive Sensing ◽  
Real World ◽  
Imaging Techniques ◽  
Optimization Methods ◽  
New Paradigm ◽  
Optimization Framework ◽  
Real Scene ◽  
Learning Techniques ◽  
Ill Posed ◽  
Magnetic Resonance Imaging Mri

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indications. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-theart techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene.

Optimization Methods in Finance

2018 ◽  
Author(s):  
Gérard Cornuéjols ◽  
Javier Peña ◽  
Reha Tütüncü
Keyword(s):  
Optimization Methods

Optimization Methods in Finance

2006 ◽  
Author(s):  
Gerard Cornuejols ◽  
Reha Tutuncu
Keyword(s):  
Optimization Methods

Economics of coated paper production made from deinked pulp

TAPPI Journal ◽  
2013 ◽  
Vol 12 (4) ◽  
pp. 19-27
Author(s):  
PATRICK HUBER ◽  
LAURENT LYANNAZ ◽  
BRUNO CARRÉ
Keyword(s):  
Mechanical Properties ◽  
Optimization Methods ◽  
Coating Process ◽  
Coated Paper ◽  
Paper Production ◽  
Base Paper

The fraction of deinked pulp for coated paper production is continually increasing, with some mills using 100% deinked pulp for the base paper. The brightness of the coated paper made from deinked pulp may be reached through a combination of more or less extensive deinking, compensated by appropriate coating, to optimize costs overall. The authors proposed general optimization methods combined with Kubelka-Munk multilayer calculations to find the most economical combination of deinking and coating process that would produce a coated paper made from DIP, at a given target brightness, while maintaining mechanical properties.

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