Single-image vignetting correction using radial gradient symmetry

In this paper, we present a novel single-image vignetting method based on the symmetric distribution of the radial gradient (RG). The radial gradient is the image gradient along the radial direction with respect to the image center. We show that the RG distribution for natural images without vignetting is generally symmetric. However, this distribution is skewed by vignetting. We develop two variants of this technique, both of which remove vignetting by minimizing asymmetry of the RG distribution. Compared with prior approaches to single-image vignetting correction, our method does not require segmentation and the results are generally better. Experiments show our technique works for a wide range of images and it achieves a speed-up of 4-5 times compared with a state-of-the-art method.

[1]  Assaf Zomet,et al.  Learning to Perceive Transparency from the Statistics of Natural Scenes , 2002, NIPS.

[2]  Stephen Lin,et al.  Single-Image Vignetting Correction , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Yoav Y. Schechner,et al.  Addressing radiometric nonidealities: a unified framework , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[5]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[6]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, ACM Trans. Graph..

[7]  Yair Weiss,et al.  Deriving intrinsic images from image sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Michael J. Black,et al.  Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Peter Meer,et al.  ROBUST TECHNIQUES FOR COMPUTER VISION , 2004 .

[10]  Bryan C. Russell,et al.  Exploiting the sparse derivative prior for super-resolution , 2003 .

[11]  Anat Levin,et al.  User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[13]  Assaf Zomet,et al.  Learning how to inpaint from global image statistics , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[14]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Aditi Majumder,et al.  Photometric Self-Calibration of a Projector-Camera System , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[17]  Stephen Lin,et al.  Radiometric Calibration from Noise Distributions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Michael J. Black,et al.  Steerable Random Fields , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[19]  Song-Chun Zhu,et al.  Prior Learning and Gibbs Reaction-Diffusion , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  William T. Freeman,et al.  What makes a good model of natural images? , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Dan B. Goldman,et al.  Vignette and exposure calibration and compensation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[22]  Masashi Baba,et al.  Photometric calibration of zoom lens systems , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[23]  Wonpil Yu,et al.  Practical anti-vignetting methods for digital cameras , 2004, IEEE Trans. Consumer Electron..

[24]  Alexander A. Sawchuk,et al.  Real-Time Correction of Intensity Nonlinearities in Imaging Systems , 1977, IEEE Transactions on Computers.

[25]  Andrew W. Fitzgibbon,et al.  Bayesian video matting using learnt image priors , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[26]  Sing Bing Kang,et al.  Can We Calibrate a Camera Using an Image of a Flat, Textureless Lambertian Surface? , 2000, ECCV.

[27]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .