A shallow convolutional neural network for blind image sharpness assessment.
文献类型：期刊论文
| 作者 | Yu, Shaode; Wu, Shibin; Wang, Lei; Jiang, Fan; Xie, Yaoqin; Li, Leida |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2017 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Blind image quality assessment can be modeled as feature extraction followed by score prediction. It necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality. This paper addresses blind image sharpness assessment by using a shallowconvolutional neural network (CNN). The network takes single feature layer to unearth intrinsic features for image sharpness representation and utilizes multilayer perceptron (MLP) to rate image quality. Different from traditional methods, CNN integrates feature extraction and score prediction into an optimization procedure and retrieves features automatically from raw images. Moreover, its prediction performance can be enhanced by replacing MLP with general regression neural network (GRNN) and support vector regression (SVR). Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/11986]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 作者单位 | PLOS ONE |
| 推荐引用方式 GB/T 7714 | Yu, Shaode,Wu, Shibin,Wang, Lei,et al. A shallow convolutional neural network for blind image sharpness assessment.[J]. PLOS ONE,2017. |
| APA | Yu, Shaode,Wu, Shibin,Wang, Lei,Jiang, Fan,Xie, Yaoqin,&Li, Leida.(2017).A shallow convolutional neural network for blind image sharpness assessment..PLOS ONE. |
| MLA | Yu, Shaode,et al."A shallow convolutional neural network for blind image sharpness assessment.".PLOS ONE (2017). |
入库方式： OAI收割
来源：深圳先进技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。