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Image Noise Level Classification Technique Based on Image Quality Assessment
Luo, Geng1; Zhao ZC(赵梓成)2; Long Q(龙潜)2; Lv, Chun1; Bao, Jie1
Source PublicationProceedings of 2020 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2020
2020-07
Pages651-656
DOI10.1109/ICPICS50287.2020.9202118
Contribution Rank第2完成单位
Indexed ByEI
Conference Name2020 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2020
Conference Date2020-07-28
Conference PlaceShenyang, China
Abstract

Image noise plays a vital role in digital image processing. However, in some specific application scenarios, random noise has an uncontrollable effect on digital image processing. Besides, a large number of hyper parameters which need to be fine-tuned can lead to inefficient projects. Therefore, we propose a Image Noise Level Classification(INLC) technique for specific application scenarios by comparing image quality assessment(IQA) methods, fitting curves and designing two neural networks. For low-accuracy, we come up with a soft way by setting a tolerance rate to achieve a higher acceptable accuracy. Experiments show that our INLC is more accurate and efficient.

Funding ProjectN/A
Funding OrganizationN/A
Language英语
Subject Area计算机科学技术
MOST Discipline Catalogue工学 ; 工学::计算机科学与技术(可授工学、理学学位)
SubtypeConference article (CA)
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN9781728198736
EI Accession Number20204309378098
EI KeywordsImage quality
EI Classification Number723.4 Artificial Intelligence - 921.6 Numerical Methods
Citation statistics
Document Type会议论文
Identifierhttp://ir.ynao.ac.cn/handle/114a53/23721
Collection丽江天文观测站(南方基地)
Affiliation1.Chengdu Fourier Electronic Technology Co., Ltd, R and D Department, Chengdu, China
2.Yunnan Observatories, Chinese Academy of Science, Kunming, China
Recommended Citation
GB/T 7714
Luo, Geng,Zhao ZC,Long Q,et al. Image Noise Level Classification Technique Based on Image Quality Assessment[C]:Institute of Electrical and Electronics Engineers Inc.,2020:651-656.
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