This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Single-Image Vignetting Correction from Gradient Distribution Symmetries
June 2013 (vol. 35 no. 6)
pp. 1480-1494
Yuanjie Zheng, University of Pennsylvania, Philadelphia
Stephen Lin, Microsoft Research Asia, Beijing
Sing Bing Kang, Microsoft Corporation, Redmond
Rui Xiao, University of Pennsylvania, Philadelphia
James C. Gee, University of Pennsylvania, Philadelphia
Chandra Kambhamettu, University of Delaware, Newark
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.
Index Terms:
Optical imaging,Adaptive optics,Optical distortion,Histograms,Estimation,Nonlinear optics,Optical sensors,nonuniformity correction,Vignetting correction,camera calibration,low-level vision,bias correction
Citation:
Yuanjie Zheng, Stephen Lin, Sing Bing Kang, Rui Xiao, James C. Gee, Chandra Kambhamettu, "Single-Image Vignetting Correction from Gradient Distribution Symmetries," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1480-1494, June 2013, doi:10.1109/TPAMI.2012.210
Usage of this product signifies your acceptance of the Terms of Use.