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	Evaluation of realistic blurring image quality by using a shallow convolutional neural network
	Yaoqing Li; Zhaoyang Wang; Guangzhe Dai; Shibin Wu; Shaode Yu; Yaoqin Xie
	2017
会议日期	2017
会议地点	澳门
英文摘要	Manifold causes of image blurring make the noreference evaluation of realistic blurred images very challenging. Previous studies indicate that handcrafted features suffer from poor representation of the intrinsic characteristics of image blurring and thus blind image sharpness assessment (BISA) is unsatisfactory. This paper explores a shallow convolutional neural network (CNN) to address this problem facilitated by data augmentation. Superior to algorithms that necessitates considerable expertise and efforts to handcraft features for optimal representation of perceptual image quality, CNN directly integrates the retrieval of intrinsic features and the prediction of image blur quality into an optimization process. Moreover, experiments on Realistic Blurring Image Database have verified that CNN advances in retrieving intrinsic features and obtains good correlation with subjective image blurring evaluations.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12208]
专题	深圳先进技术研究院_医工所
作者单位	2017
推荐引用方式 GB/T 7714	Yaoqing Li,Zhaoyang Wang,Guangzhe Dai,et al. Evaluation of realistic blurring image quality by using a shallow convolutional neural network[C]. 见:. 澳门. 2017.

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