کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5449017 | 1512519 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimized multiple linear mappings for single image super-resolution
ترجمه فارسی عنوان
بهینه سازی نقشه های چندگانه برای یک تصویر فوق العاده با وضوح
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی مواد
مواد الکترونیکی، نوری و مغناطیسی
چکیده انگلیسی
Learning piecewise linear regression has been recognized as an effective way for example learning-based single image super-resolution (SR) in literature. In this paper, we employ an expectation-maximization (EM) algorithm to further improve the SR performance of our previous multiple linear mappings (MLM) based SR method. In the training stage, the proposed method starts with a set of linear regressors obtained by the MLM-based method, and then jointly optimizes the clustering results and the low- and high-resolution subdictionary pairs for regression functions by using the metric of the reconstruction errors. In the test stage, we select the optimal regressor for SR reconstruction by accumulating the reconstruction errors of m-nearest neighbors in the training set. Thorough experimental results carried on six publicly available datasets demonstrate that the proposed SR method can yield high-quality images with finer details and sharper edges in terms of both quantitative and perceptual image quality assessments.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Optics Communications - Volume 404, 1 December 2017, Pages 169-176
Journal: Optics Communications - Volume 404, 1 December 2017, Pages 169-176
نویسندگان
Kaibing Zhang, Jie Li, Zenggang Xiong, Xiuping Liu, Xinbo Gao,