کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6940214 | 1450008 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
High-quality face image generated with conditional boundary equilibrium generative adversarial networks
ترجمه فارسی عنوان
تصویر چهره با کیفیت بالا تولید شده با شبکه های محرک تولید تعادلی متعادل مرزی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
We propose a novel single face image super-resolution method, which is named Face Conditional Generative Adversarial Network (FCGAN), based on boundary equilibrium generative adversarial networks. Without taking any prior facial information, our approach combines the pixel-wise L1 loss and GAN loss to optimize our super-resolution model and to generate a high-quality face image from a low-resolution one robustly (with upscaling factor 4â¯Ãâ¯). Additionally, Compared with existing peer researches, both training and testing phases of FCGAN are end-to-end pipeline without pre/post-processing. To enhance the convergence speed and strengthen feature propagation, the Generator and Discriminator networks are designed with a skip-connection architecture, and both using an auto-encoder structure. Quantitative experiments demonstrate that our model achieves competitive performance compared with the state-of-the-art models based on both visual quality and quantitative criterions. We believe this high-quality face image generated method can impact many applications in face identification and intelligent monitor.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 111, 1 August 2018, Pages 72-79
Journal: Pattern Recognition Letters - Volume 111, 1 August 2018, Pages 72-79
نویسندگان
Bin Huang, Weihai Chen, Xingming Wu, Chun-Liang Lin, Ponnuthurai Nagaratnam Suganthan,