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Efficient image sensor noise estimation via iterative re-weighted least squares
Dong L.2; Zhou J.2; Zhai G.1
2017-08-28
Conference NameIEEE International Conference on Multimedia and Expo (ICME)
Source PublicationProceedings - IEEE International Conference on Multimedia and Expo
Pages1326-1331
Conference DateJUL 10-14, 2017
Conference PlaceHong Kong, HONG KONG
Abstract

Noise estimation is crucial in many image processing algorithms such as image denoising. Conventionally, the noise is assumed as signal-independent additive white Gaussian process. However, for the real raw-data of imaging sensors, the present noise is better modeled as signal-dependent noise. In this work, we propose an efficient image sensor noise estimation method based on iterative re-weighted least squares optimization. Specifically, the image patches are first clustered into different groups, each of which will generate a data sample. To fit those observations robustly, we introduce a weighting matrix to reflect the credibility of each sample. Unfortunately, this setting of weighting matrix in turn depends on the unknown noise parameters. We then develop an iterative re-weighted least squares optimization procedure, in which the weighting matrix and parameter estimates can be updated alternately. Experimental results show that our method outperforms the state-of-the-art works, in terms of both estimation accuracy and computational efficiency.

KeywordNoise Estimation Signal-dependent Noise
DOI10.1109/ICME.2017.8019427
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000426984300218
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Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Shanghai Jiao Tong University
2.Universidade de Macau
Recommended Citation
GB/T 7714
Dong L.,Zhou J.,Zhai G.. Efficient image sensor noise estimation via iterative re-weighted least squares[C],2017:1326-1331.
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