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
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 4 × ). 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
نویسندگان
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