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Content-Aware Compressive Sensing Recovery Using Laplacian Scale Mixture Priors and Side Information
المؤلفون المشاركون
Xie, Zhonghua
Ma, Lihong
Liu, Lingjun
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-01-29
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
Nonlocal methods have shown great potential in many image restoration tasks including compressive sensing (CS) reconstruction through use of image self-similarity prior.
However, they are still limited in recovering fine-scale details and sharp features, when rich repetitive patterns cannot be guaranteed; moreover the CS measurements are corrupted.
In this paper, we propose a novel CS recovery algorithm that combines nonlocal sparsity with local and global prior, which soften and complement the self-similarity assumption for irregular structures.
First, a Laplacian scale mixture (LSM) prior is utilized to model dependencies among similar patches.
For achieving group sparsity, each singular value of similar packed patches is modeled as a Laplacian distribution with a variable scale parameter.
Second, a global prior and a compensation-based sparsity prior of local patch are designed in order to maintain differences between packed patches.
The former refers to a prediction which integrates the information at the independent processing stage and is used as side information, while the latter enforces a small (i.e., sparse) prediction error and is also modeled with the LSM model so as to obtain local sparsity.
Afterward, we derive an efficient algorithm based on the expectation-maximization (EM) and approximate message passing (AMP) frame for the maximum a posteriori (MAP) estimation of the sparse coefficients.
Numerical experiments show that the proposed method outperforms many CS recovery algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xie, Zhonghua& Ma, Lihong& Liu, Lingjun. 2018. Content-Aware Compressive Sensing Recovery Using Laplacian Scale Mixture Priors and Side Information. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1208732
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xie, Zhonghua…[et al.]. Content-Aware Compressive Sensing Recovery Using Laplacian Scale Mixture Priors and Side Information. Mathematical Problems in Engineering No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1208732
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xie, Zhonghua& Ma, Lihong& Liu, Lingjun. Content-Aware Compressive Sensing Recovery Using Laplacian Scale Mixture Priors and Side Information. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1208732
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208732
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