Estimating vignetting function from a single image for image authentication

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.

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

[2]  Aapo Hyvärinen,et al.  Natural Image Statistics - A Probabilistic Approach to Early Computational Vision , 2009, Computational Imaging and Vision.

[3]  Hany Farid,et al.  Exposing digital forgeries through chromatic aberration , 2006, MM&Sec '06.

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

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

[6]  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).

[7]  Edward H. Adelson,et al.  Noise removal via Bayesian wavelet coring , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[8]  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).

[9]  G. Casella,et al.  Statistical Inference , 2003, Encyclopedia of Social Network Analysis and Mining.

[10]  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.

[11]  Mo Chen,et al.  Determining Image Origin and Integrity Using Sensor Noise , 2008, IEEE Transactions on Information Forensics and Security.

[12]  Min Wu,et al.  Digital image forensics via intrinsic fingerprints , 2008, IEEE Transactions on Information Forensics and Security.

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

[14]  Eero P. Simoncelli,et al.  Reducing statistical dependencies in natural signals using radial Gaussianization , 2008, NIPS.

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

[16]  Edward H. Adelson,et al.  The Laplacian Pyramid as a Compact Image Code , 1983, IEEE Trans. Commun..

[17]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

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

[19]  Thomas Gloe,et al.  Feature-Based Camera Model Identification Works in Practice , 2009, Information Hiding.

[20]  Stephen Lin,et al.  Single-image vignetting correction using radial gradient symmetry , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  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).

[22]  D. Ruderman.  The statistics of natural images , 1994 .

[23]  J. H. van Hateren,et al.  Modelling the Power Spectra of Natural Images: Statistics and Information , 1996, Vision Research.

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

[25]  Chi-Keung Tang,et al.  Tensor voting for image correction by global and local intensity alignment , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Shih-Fu Chang,et al.  Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[27]  Eero P. Simoncelli,et al.  Differentiation of discrete multidimensional signals , 2004, IEEE Transactions on Image Processing.

[28]  Eero P. Simoncelli,et al.  Differentiation of Discrete Multi-Dimensional Signals , 2004 .

[29]  R. Baddeley,et al.  Searching for filters with 'interesting' output distributions: an uninteresting direction to explore? , 1996, Network.

[30]  Hany Farid,et al.  Photo Fakery and Forensics , 2009, Adv. Comput..

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

[32]  Alin C. Popescu,et al.  Exposing digital forgeries in color filter array interpolated images , 2005, IEEE Transactions on Signal Processing.

[33]  Nasir D. Memon,et al.  Blind source camera identification , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[34]  G. MallatS..  A Theory for Multiresolution Signal Decomposition , 1989 .

[35]  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.

[36]  Rudolf Kingslake,et al.  Lens Design Fundamentals , 1978 .