A Review on No-reference Quality Assessment for Blurred Image

被引：0
作者：
Chen J. [1 ,2 ,3 ]
Li S.-Y. [1 ]
Lin L. [1 ,2 ]
Wang M. [1 ]
Li Z.-Y. [3 ]
机构：
[1] School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou
[2] National Demonstration Center for Experimental Electronic Information and Electrical Technology Education (Fujian University of Technology), Fuzhou
[3] Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2022年 / 48卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Blurred image; Database; Image quality assessment; No-reference image quality assessment;
D O I：
10.16383/j.aas.c201030
中图分类号：
TP392 [各种专用数据库];
学科分类号：
摘要：
The blurriness distortion of image affects information perception, acquisition and subsequent processing. No-reference blurred image quality assessment is one of main research directions for the problem. This paper analyzes the relevant technique development of no-reference blurred image quality assessment in recent 20 years. Firstly, combining with main databases, different types of blurriness distortions are described. Secondly, main methods for no-reference blurred image quality assessment are classified and analyzed in detail. Thirdly, performance measures for no-reference blurred image assessment are introduced. Then, the typical databases, performance measures and methods are introduced for performance comparisons. Finally, the relevant technologies and development trends of no-reference blurred image assessment are summarized and prospected. Copyright ©2022 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：689 / 711
页数：22
相关论文
共 161 条
[1]  
Jayageetha J, Vasanthanayaki C., Medical image quality assessment using CSO based deep neural network, Journal of Medical Systems, 42, 11, (2018)
[2]  
Ma J J, Nakarmi U, Kin C Y S, Sandino C M, Cheng J Y, Syed A B, Et al., Diagnostic image quality assessment and classification in medical imaging: Opportunities and challenges, Proceedings of the 17th International Symposium on Biomedical Imaging (ISBI), pp. 337-340, (2020)
[3]  
Chen G B, Zhai M T., Quality assessment on remote sensing image based on neural networks, Journal of Visual Communication and Image Representation, 63, (2019)
[4]  
Hombalimath A, Manjula H T, Khanam A, Girish K., Image quality assessment for iris recognition, International Journal of Scientific and Research Publications, 8, 6, pp. 100-103, (2018)
[5]  
Zhai G T, Min X K., Perceptual image quality assessment: A survey, Science China Information Sciences, 63, 11, (2020)
[6]  
Wang Ye-Ru, Research on Auto-focus Methods Based on Digital Imaging Processing, (2018)
[7]  
You Yu-Hu, Liu Tong, Liu Jia-Wen, Survey of the auto-focus methods based on image processing, Laser and Infrared, 43, 2, pp. 132-136, (2013)
[8]  
Cannon M., Blind deconvolution of spatially invariant image blurs with phase, IEEE Transactions on Acoustics, Speech, and Signal Processing, 24, 1, pp. 58-63, (1976)
[9]  
Tekalp A M, Kaufman H, Woods J W., Identification of image and blur parameters for the restoration of noncausal blurs, IEEE Transactions on Acoustics, Speech, and Signal Processing, 34, 4, pp. 963-972, (1986)
[10]  
Pavlovic G, Tekalp A M., Maximum likelihood parametric blur identification based on a continuous spatial domain model, IEEE Transactions on Image Processing, 1, 4, pp. 496-504, (1992)
12345678910