2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | MLR-APPL-IP-6.10 | ||
| Paper Title | PROGRESSIVE FACE SUPER-RESOLUTION WITH NON-PARAMETRIC FACIAL PRIOR ENHANCEMENT | ||
| Authors | Jonghyun Kim, Gen Li, Sungkyunkwan University, Republic of Korea; Cheolkon Jung, Xidian University, China; Joongkyu Kim, Sungkyunkwan University, Republic of Korea | ||
| Session | MLR-APPL-IP-6: Machine learning for image processing 6 | ||
| Location | Area E | ||
| Session Time: | Tuesday, 21 September, 15:30 - 17:00 | ||
| Presentation Time: | Tuesday, 21 September, 15:30 - 17:00 | ||
| Presentation | Poster | ||
| Topic | Applications of Machine Learning: Machine learning for image processing | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | The main challenge of face super-resolution is to overcome facial distortions in an upscaling process. Recent works have utilized facial priors such as facial landmarks and component maps to generate a precise super-resolved image. However, the facial priors are estimated from the ground-truth and deep neural networks. Thus, recent works based on the facial priors are not only limited to specific datasets including the ground-truth, but also need sub-networks to extract facial priors. To solve these problems, we propose a progressive face super-resolution network with non-parametric facial prior enhancement, called as NPFNet, which extracts and highlights facial components without any tricks, such as the ground-truth and deep neural networks. The self-extraction module facilitates our network to fully utilize facial distinct features to enhance super-resolved images with a parameter-free operation. Extensive experiments on CelebA and VGGFace2 demonstrate that the proposed method outperforms state-of-the-art face super-resolution methods in terms of visual quality and quantitative measurements. | ||