Estimating demosaicing algorithms using image noise variance

We propose a method for estimating demosaicing algorithms from image noise variance. We show that the noise variance in interpolated pixels becomes smaller than that of directly observed pixels without interpolation. Our method capitalizes on the spatial variation of image noise variance in demosaiced images to estimate the color filter array patterns and demosaicing algorithms. We verify the effectiveness of the proposed method using various images demosaiced with different demosaicing algorithms extensively.

[1]  Lei Zhang,et al.  Image demosaicing: a systematic survey , 2008, Electronic Imaging.

[2]  Richard Szeliski,et al.  Noise Estimation from a Single Image , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[4]  Youngbae Hwang,et al.  Sensor noise modeling using the Skellam distribution: Application to the color edge detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Nasir D. Memon,et al.  Classification of digital camera-models based on demosaicing artifacts , 2008, Digit. Investig..

[6]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[7]  Katsushi Ikeuchi,et al.  Estimating camera response functions using probabilistic intensity similarity , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Katsushi Ikeuchi,et al.  Estimating Radiometric Response Functions from Image Noise Variance , 2008, ECCV.

[9]  T. Treibitz,et al.  Recovery limits in pointwise degradation , 2009, 2009 IEEE International Conference on Computational Photography (ICCP).

[10]  Chi-Yi Tsai,et al.  Heterogeneity-Projection Hard-Decision Color Interpolation Using Spectral-Spatial Correlation , 2007, IEEE Transactions on Image Processing.

[11]  Stephen Lin,et al.  A Probabilistic Intensity Similarity Measure based on Noise Distributions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Tsuhan Chen,et al.  Image authentication by detecting traces of demosaicing , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[13]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005 .

[14]  Thomas W. Parks,et al.  Adaptive homogeneity-directed demosaicing algorithm , 2005, IEEE Transactions on Image Processing.

[15]  Andrew C. Gallagher.  Detection of linear and cubic interpolation in JPEG compressed images , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

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

[17]  Edward Y. Chang,et al.  Color filter array recovery using a threshold-based variable number of gradients , 1999, Electronic Imaging.