کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5449015 | 1512519 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Image sparse representation with local ARMA and nonlocal self-similarity regularizations for super-resolution
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی مواد
مواد الکترونیکی، نوری و مغناطیسی
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چکیده انگلیسی
Since the single image super-resolution (SR) is an extremely ill posed problem, we introduce a novel auto-regressive moving average (ARMA) model-based regularization term into the spare representation-based framework to deal with it in this paper. In our framework, we have a dual regularization. Firstly, we use the ARMA models trained from external samples to establish a regularization term. ARMA model-based regularization serves as a local constraint. Secondly, we introduce the nonlocal (NL) self-similarity as another regularization term. Both the local and the NL regularizations are unified into the sparse representation-based framework. Finally, extensive experiments verify the effectiveness of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optics Communications - Volume 404, 1 December 2017, Pages 155-162
Journal: Optics Communications - Volume 404, 1 December 2017, Pages 155-162
نویسندگان
Weirong Liu, Chaopeng Zhang, Jie Liu, Chaorong Liu,