Single-image vignetting correction from gradient distribution symmetries

We present novel techniques for single-image vignetting correction based on symmetries of two forms of image gradients: semicircular tangential gradients (SCTG) and radial gradients (RG). For a given image pixel, an SCTG is an image gradient along the tangential direction of a circle centered at the...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence. - 1979. - 35(2013), 6 vom: 15. Juni, Seite 1480-94
1. Verfasser: Zheng, Yuanjie (VerfasserIn)
Weitere Verfasser: Lin, Stephen, Kang, Sing Bing, Xiao, Rui, Gee, James C, Kambhamettu, Chandra
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2013
Zugriff auf das übergeordnete Werk:IEEE transactions on pattern analysis and machine intelligence
Schlagworte:Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.
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520 |a We present novel techniques for single-image vignetting correction based on symmetries of two forms of image gradients: semicircular tangential gradients (SCTG) and radial gradients (RG). For a given image pixel, an SCTG is an image gradient along the tangential direction of a circle centered at the presumed optical center and passing through the pixel. An RG is an image gradient along the radial direction with respect to the optical center. We observe that the symmetry properties of SCTG and RG distributions are closely related to the vignetting in the image. Based on these symmetry properties, we develop an automatic optical center estimation algorithm by minimizing the asymmetry of SCTG distributions, and also present two methods for vignetting estimation based on minimizing the asymmetry of RG distributions. In comparison to prior approaches to single-image vignetting correction, our methods do not rely on image segmentation and they produce more accurate results. Experiments show our techniques to work well for a wide range of images while achieving a speed-up of 3-5 times compared to a state-of-the-art method 
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650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Lin, Stephen  |e verfasserin  |4 aut 
700 1 |a Kang, Sing Bing  |e verfasserin  |4 aut 
700 1 |a Xiao, Rui  |e verfasserin  |4 aut 
700 1 |a Gee, James C  |e verfasserin  |4 aut 
700 1 |a Kambhamettu, Chandra  |e verfasserin  |4 aut 
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