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Blur and image restoration of nonlinearly degraded images using neural networks based on modified nonlinear ARMA model
Joint Authors
Shaima, T. A.
Qurayshi, I. M.
Jalil, A.
Naveed, A.
Source
The Arabian Journal for Science and Engineering. Section B, Engineering
Issue
Vol. 32, Issue 1B (30 Apr. 2007), pp.67-83, 17 p.
Publisher
King Fahd University of Petroleum and Minerals
Publication Date
2007-04-30
Country of Publication
Saudi Arabia
No. of Pages
17
Main Subjects
Information Technology and Computer Science
Topics
Abstract AR
تم هذا البحث اقتراح خوارزمية لترميم الصور, و ذلك لتحديد اقترانات فير خطية و غير محلية باستخدام عصبيات الشبكة الصناعية, و تحتوي الصور و عملية التلف على ظواهر خطية و غير خطية.
يدمج الأنموذج الشبكي العصبي شبكة ذاتية المواءمة مع عملية عشوائية جاوسية استخدمت لترميم الصورة المشوشة, و كذلك على هيئة اقتران تشويشي.
و قد قمنا بتمثيل الصور المشوشة على شكل شبكة مشاركة غير خطية و مستمرة.
يحدد الجزء ذاتي الاشتراك معاملات انموذج الصورة و الجزء المشارك الذي يحدد اقتران التشويش في عملية تلف الصور.
و يقدم الترتيب الذاتي المقترح إمكانية حل مسألة الأعمى للصور.
و قد قمنا بتطبيق عملية التقدير و الترميم باستخدام خوارزمية تكرارية التحذر لتقليل اقتران الخطأ.
Abstract EN
In this paper, an image restoration algorithm is proposed to identify nonlinear and noncausal blur funclon using artificial neural networks.
Image and degradation processes include both linear and nonlinear phenomena.
The proposed neural network model, which combines an adaptive auto-associative network with a random Gaussian process, is used to restore the blurred image and blur function, simultaneously.
The noisy and blurred images are modeled as nonlinear continuous associative networks.
The auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the image degradation process.
The self-organization like structure of the proposed neural network provides the potential solution of the blind image restoration problem.
The estimation and restoration are implemented by using an iterative gradient based algorithm to minimize the error function.
American Psychological Association (APA)
Shaima, T. A.& Qurayshi, I. M.& Jalil, A.& Naveed, A.. 2007. Blur and image restoration of nonlinearly degraded images using neural networks based on modified nonlinear ARMA model. The Arabian Journal for Science and Engineering. Section B, Engineering،Vol. 32, no. 1B, pp.67-83.
https://search.emarefa.net/detail/BIM-358995
Modern Language Association (MLA)
Shaima, T. A.…[et al.]. Blur and image restoration of nonlinearly degraded images using neural networks based on modified nonlinear ARMA model. The Arabian Journal for Science and Engineering. Section B, Engineering Vol. 32, no. 1B (Apr. 2007), pp.67-83.
https://search.emarefa.net/detail/BIM-358995
American Medical Association (AMA)
Shaima, T. A.& Qurayshi, I. M.& Jalil, A.& Naveed, A.. Blur and image restoration of nonlinearly degraded images using neural networks based on modified nonlinear ARMA model. The Arabian Journal for Science and Engineering. Section B, Engineering. 2007. Vol. 32, no. 1B, pp.67-83.
https://search.emarefa.net/detail/BIM-358995
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 82-83
Record ID
BIM-358995