New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization
المؤلفون المشاركون
Tang, Xuan
Likassa, Habte Tadesse
Xian, Wen
المصدر
International Journal of Mathematics and Mathematical Sciences
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-06
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
In this work, a new robust regularized shrinkage regression method is proposed to recover and align high-dimensional images via affine transformation and Tikhonov regularization.
To be more resilient with occlusions and illuminations, outliers, and heavy sparse noises, the new proposed approach incorporates novel ideas affine transformations and Tikhonov regularization into high-dimensional images.
The highly corrupted, distorted, or misaligned images can be adjusted through the use of affine transformations and Tikhonov regularization term to ensure a trustful image decomposition.
These novel ideas are very essential, especially in pruning out the potential impacts of annoying effects in high-dimensional images.
Then, finding optimal variables through a set of affine transformations and Tikhonov regularization term is first casted as mathematical and statistical convex optimization programming techniques.
Afterward, a fast alternating direction method for multipliers (ADMM) algorithm is applied, and the new equations are established to update the parameters involved and the affine transformations iteratively in the form of the round-robin manner.
Moreover, the convergence of these new updating equations is scrutinized as well, and the proposed method has less time computation as compared to the state-of-the-art works.
Conducted simulations have shown that the new robust method surpasses to the baselines for image alignment and recovery relying on some public datasets.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Likassa, Habte Tadesse& Xian, Wen& Tang, Xuan. 2020. New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization. International Journal of Mathematics and Mathematical Sciences،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1172591
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Likassa, Habte Tadesse…[et al.]. New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization. International Journal of Mathematics and Mathematical Sciences No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1172591
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Likassa, Habte Tadesse& Xian, Wen& Tang, Xuan. New Robust Regularized Shrinkage Regression for High-Dimensional Image Recovery and Alignment via Affine Transformation and Tikhonov Regularization. International Journal of Mathematics and Mathematical Sciences. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1172591
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1172591
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر