Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel method of cleaning up facial imperfections such as bumps and blemishes that may detract
from a pleasing digital portrait. Contrasting with traditional methods which tend to blur facial details, our method fully
retains fine scale skin textures (pores etc.) of the subject. Our key idea is to find a quantity, namely normalized local
energy, to capture different characteristics of fine scale details and distractions, based on empirical mode decomposition,
and then build a quantitative measurement of facial skin appearance which characterizes both imperfections and facial details
in a unified framework. Finally, we use the quantitative measurement as a guide to enhance facial skin. We also introduce
a few high-level, intuitive parameters for controlling the amount of enhancement. In addition, an adaptive local mean and
neighborhood limited empirical mode decomposition algorithm is also developed to improve in two respects the performance of
empirical mode decomposition. It can effectively avoid the gray spots effect commonly associated with traditional empirical
mode decomposition when dealing with high-nonstationary images. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s11390-009-9245-0 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
image enhancement,empirical mode decomposition,normalized local energy | Computer vision,Normalization (statistics),Computer science,Skin appearance,Artificial intelligence,Hilbert–Huang transform | Journal |
Volume | Issue | ISSN |
24 | 3 | 1860-4749 |
Citations | PageRank | References |
1 | 0.37 | 13 |
Authors | ||
14 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan-Li Liu | 1 | 44 | 2.06 |
| XiaoGang Xu | 2 | 74 | 6.20 |
| Yan-Wen Guo | 3 | 348 | 39.32 |
| Jin Wang | 4 | 61 | 6.40 |
| Xin Duan | 5 | 1 | 0.37 |
| Xi Chen | 6 | 74 | 26.21 |
| Qunsheng Peng | 7 | 1193 | 101.63 |
| 刘艳丽 | 8 | 44 | 2.06 |
| 徐晓刚 | 9 | 1 | 0.37 |
| 郭延文 | 10 | 44 | 2.06 |
| 王进 | 11 | 44 | 2.06 |
| 段鑫 | 12 | 1 | 0.37 |
| 陈曦 | 13 | 44 | 2.06 |
| 彭群生 | 14 | 45 | 2.74 |