Automatic estimation and removal of noise from a single image

Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches are not fully automatic and cannot effectively remove color noise produced by todays CCD digital camera. In this paper, we propose a unified framework...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 30(2008), 2 vom: 15. Feb., Seite 299-314
1. Verfasser: Liu, Ce (VerfasserIn)
Weitere Verfasser: Szeliski, Richard, Bing Kang, Sing, Zitnick, C Lawrence, Freeman, William T
Format: Aufsatz
Sprache:English
Veröffentlicht: 2008
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Journal Article
LEADER 01000naa a22002652 4500
001 NLM17578907X
003 DE-627
005 20231223142744.0
007 tu
008 231223s2008 xx ||||| 00| ||eng c
028 5 2 |a pubmed24n0586.xml 
035 |a (DE-627)NLM17578907X 
035 |a (NLM)18084060 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Liu, Ce  |e verfasserin  |4 aut 
245 1 0 |a Automatic estimation and removal of noise from a single image 
264 1 |c 2008 
336 |a Text  |b txt  |2 rdacontent 
337 |a ohne Hilfsmittel zu benutzen  |b n  |2 rdamedia 
338 |a Band  |b nc  |2 rdacarrier 
500 |a Date Completed 12.03.2008 
500 |a Date Revised 17.12.2007 
500 |a published: Print 
500 |a Citation Status PubMed-not-MEDLINE 
520 |a Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches are not fully automatic and cannot effectively remove color noise produced by todays CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as a function of image brightness. We then estimate an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms 
650 4 |a Journal Article 
700 1 |a Szeliski, Richard  |e verfasserin  |4 aut 
700 1 |a Bing Kang, Sing  |e verfasserin  |4 aut 
700 1 |a Zitnick, C Lawrence  |e verfasserin  |4 aut 
700 1 |a Freeman, William T  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on pattern analysis and machine intelligence  |d 1979  |g 30(2008), 2 vom: 15. Feb., Seite 299-314  |w (DE-627)NLM098212257  |x 1939-3539  |7 nnns 
773 1 8 |g volume:30  |g year:2008  |g number:2  |g day:15  |g month:02  |g pages:299-314 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 30  |j 2008  |e 2  |b 15  |c 02  |h 299-314