A shallow convolutional neural network for blind image sharpness assessment.

	A shallow convolutional neural network for blind image sharpness assessment.
	Yu, Shaode; Wu, Shibin; Wang, Lei; Jiang, Fan; Xie, Yaoqin; Li, Leida
刊名	PLOS ONE
	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
内容类型	期刊论文
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).