کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7117010 | 1461214 | 2016 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Video super-resolution reconstruction based on deep convolutional neural network and spatio-temporal similarity
ترجمه فارسی عنوان
بازسازی تصویر با وضوح بالا بر اساس شبکه عصبی کانولوشن عمیق و شباهت فضایی و زمانی
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موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی برق و الکترونیک
چکیده انگلیسی
Existing learning-based super-resolution (SR) reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network (CNN) and spatio-temporal similarity (STCNN-SR) was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not only the mapping relationship among associated low-resolution (LR) and high-resolution (HR) image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: The Journal of China Universities of Posts and Telecommunications - Volume 23, Issue 5, October 2016, Pages 68-81
Journal: The Journal of China Universities of Posts and Telecommunications - Volume 23, Issue 5, October 2016, Pages 68-81
نویسندگان
Li Linghui, Du Junping, Liang Meiyu, Ren Nan, Fan Dan,