Name
Title | ||
|---|---|---|
OPLS-SR: A novel face super-resolution learning method using orthonormalized coherent features |
Abstract | ||
|---|---|---|
Face super-resolution (FSR) is an effective way to deal with low-resolution (LR) face images, which can infer the latent high-resolution (HR) face images from the LR inputs. In contrast with traditional FSR methods such as interpolation, learning-based methods generate more realistic HR images of LR faces by exploiting the relationship between HR and LR images. In this paper, we propose a novel FSR learning approach based on orthonormalized partial least squares referred to as OPLS-SR. It first learns a latent coherent feature space of low-dimensional HR and LR face embeddings via a recursive optimization, and then super-resolves the LR face images through global face reconstruction and facial detail compensation. Experimental results on the CAS-PEAL-R1 and FERET face databases have demonstrated the effectiveness of the proposed OPLS-SR method in terms of quantitative and qualitative evaluations. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.ins.2021.01.082 | Information Sciences |
Keywords | DocType | Volume |
Face super-resolution,Partial least squares,Low resolution,Face hallucination,Orthogonality | Journal | 561 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.35 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunhao Yuan | 1 | 19 | 4.64 |
| Jin Li | 2 | 1 | 1.03 |
| Yun Li | 3 | 443 | 53.24 |
| Jipeng Qiang | 4 | 42 | 13.63 |
| Bin Li | 5 | 318 | 30.27 |
| Wankou Yang | 6 | 199 | 26.33 |
| Furong Peng | 7 | 1 | 0.35 |