Siwei Lyu
ABSTRACT
Vignetting is the phenomenon of reduced brightness in an image at the peripheral region compared to the central region. As patterns of vignetting are characteristics of lens models, they can be used to authenticate digital images for forensic analysis. In this paper, we describe a new method for model based single image vignetting estimation and correction. We use the statistical properties of natural images in the discrete derivative domains and formulate the vignetting estimation problem as a maximum likelihood estimation. We further provide a simple and efficient procedure for better initialization of the numerical optimization. Empirical evaluations of the proposed method using synthesized and real vignetted images show significant gain in both performance and running efficiency in correcting vignetting from digital images, and the estimated vignetting functions are shown to be effective in classifying different lens models.
- A. Levin and Y. Weiss. User assisted separation of reflections from a single image using a sparsity prior. In ECCV, 2004.Google Scholar
Cross Ref
- N. Asada, A. Amano, and M. Baba. Photometric calibration of zoom lens systems. In ICPR, 1996. Google ScholarDigital Library
- R. Baddeley. Searching for filters with "interesting" output distributions: an uninteresting direction to explore. Network, 7:409--421, 1996.Google Scholar
- P. J. Burt and E. H. Adelson. The Laplacian pyramid as a compact image code. IEEE Transactions on Communication, 31(4):532--540, 1981.Google ScholarCross Ref
- G. Casella and R. L. Berger. Statistical Inference. Duxbury Press, 2nd edition, 2001.Google Scholar
- M. Chen, J. Fridrich, M. Goljan, and J. Lukas. Determining image origin and integrity using sensor noise. IEEE Transactions on Information Forensics and Security, 3(1):74--90, March 2008. Google ScholarDigital Library
- H. Farid. Photo fakery and forensics. In Advances in Computers. 2009. (to appear).Google ScholarCross Ref
- H. Farid and E.P. Simoncelli. Differentiation of discrete multi-dimensional signals. IEEE Trans. Image Proc., 13(4):496--508, 2004. Google ScholarDigital Library
- R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. In ACM SIGGRAPH, 2006. Google ScholarDigital Library
- Thomas Gloe, Karsten Borowka, and Antje Winkler. Feature-based camera model identification works in practice results of a comprehensive evaluation study. In S. Katzenbeisser and A.-R. Sadeghi, editors, IH 2009, LNCS 5806,, pages 262--276, 2009. Google ScholarDigital Library
- D. B. Goldman and J. H. Chen. Vignette and exposure cal- ibration and compensation. In ICCV, 2005. Google ScholarDigital Library
- Y-F. Hsu and S-F. Chang. Image splicing detection using camera response function consistency and automatic segmentation. In International Conference on Multimedia and Expo, Beijing, China, 2007.Google ScholarCross Ref
- J. Huang and D. Mumford. Statistics of natural images and models. In CVPR, 1999.Google Scholar
- A. Hyvärinen, J. Hurri, and P. O. Hoyer. Natural Image Statistics: A probabilistic approach to early computational vision. Springer, 2009. Google ScholarDigital Library
- J. Jia and C.-K. Tang. Tensor voting for image correction by global and local intensity alignment. IEEE Trans. PAMI, 27(1):36--50, 2005. Google ScholarDigital Library
- M.K. Johnson and H. Farid. Exposing digital forgeries through chromatic aberration. In ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006. Google ScholarDigital Library
- S. Kang and R. Weiss. Can we calibrate a camera using an image of a flat textureless lambertian surface? In ECCV, 2000. Google ScholarDigital Library
- M. Kharrazi, H.T. Sencar, and N Memon. Blind source camera identification. In Proceedings of the 2004 IEEE International Conference on Image Processing (ICIP 2004),, pages 709--712, 2004.Google ScholarCross Ref
- Rudolf Kingslake and R. Barry Johnson. Lens Design Fundamentals. Academic Press, 2nd edition, 2009.Google Scholar
- A. Levin, A. Zomet, and Y. Weiss. Learning how to inpaint from global image statistics. In ICCV, 2003. Google ScholarDigital Library
- A. Litvinov and Y. Y. Schechner. Addressing radiometric nonidealities: A unified framework. In CVPR, 2005. Google ScholarDigital Library
- S. Lyu and E. P. Simoncelli. Reducing statistical dependencies in natural signals using radial Gaussianization. In NIPS, 2008.Google Scholar
- S G Mallat. A theory for multiresolution signal decomposition: The wavelet representation. 11:674--693, 1989. Google ScholarDigital Library
- D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001.Google ScholarCross Ref
- A.C. Popescu and H. Farid. Exposing digital forgeries in color filter array interpolated images. IEEE Transactions on Signal Processing, 53(10):3948--3959, 2005. Google ScholarDigital Library
- Sidney F. Ray. Applied photographic optics. Focal Press, 3rd edition, 2002.Google Scholar
- S. Roth and M. Black. Fields of experts: A framework for learning image priors. In CVPR, 2005. Google ScholarDigital Library
- D. Ruderman. The statistics of natural images. Network: Comp. in Neural Sys., 5:598--605, 1994.Google ScholarCross Ref
- A. A. Sawchuk. Real-time correction of intensity nonlinearities in imaging systems. IEEE Trans. Computers, 26(1):34--39, 1977.Google ScholarDigital Library
- E. P. Simoncelli and E. H. Adelson. Noise removal via Bayesian wavelet coring. In ICIP, 1996.Google ScholarCross Ref
- A. Swaminathan, Min Wu, and K.J.R. Liu. Digital image forensics via intrinsic fingerprints. IEEE Transactions on Information Forensics and Security, 3(1):101--117, March 2008. Google ScholarDigital Library
- A van der Schaaf and J H van Hateren. Modelling the power spectra of natural images: Statistics and information. Vision Research, 28(17):2759--2770, 1996.Google ScholarCross Ref
- W. Yu. Practical anti-vignetting methods for digital cameras. IEEE Trans. on Cons. Elect, 50(2):975--983, 2004. Google ScholarDigital Library
- Yuanjie Zheng, Stephen Lin, and Sing Bing Kang. Single-image vignetting correction. In CVPR, 2006. Google ScholarDigital Library
- Yuanjie Zheng, Jingyi Yu, Stephen Lin, Sing Bing Kang, and Chandra Kambhamettu. Single-image vignetting correction using radial gradient symmetry. In CVPR, 2008.Google Scholar
Index Terms
- Estimating vignetting function from a single image for image authentication
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