What is the space of camera response functions?

Many vision applications require precise measurement of scene radiance. The function relating scene radiance to image brightness is called the camera response. We analyze the properties that all camera responses share. This allows us to find the constraints that any response function must satisfy. These constraints determine the theoretical space of all possible camera responses. We have collected a diverse database of real-world camera response functions (DoRF). Using this database we show that real-world responses occupy a small part of the theoretical space of all possible responses. We combine the constraints from our theoretical space with the data from DoRF to create a low-parameter Empirical Model of Response (EMoR). This response model allows us to accurately interpolate the complete response function of a camera from a small number of measurements obtained using a standard chart. We also show that the model can be used to accurately estimate the camera response from images of an arbitrary scene taken using different exposures. The DoRF database and the EMoR model can be downloaded at http://www.cs.columbia.edu/CAVE.

[1]  Glenn Healey,et al.  Radiometric CCD camera calibration and noise estimation , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Graham D. Finlayson,et al.  Color by Correlation: A Simple, Unifying Framework for Color Constancy , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Steve Mann,et al.  Comparametric equations with practical applications in quantigraphic image processing , 2000, IEEE Trans. Image Process..

[5]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[6]  Steve Mann,et al.  ON BEING `UNDIGITAL' WITH DIGITAL CAMERAS: EXTENDING DYNAMIC RANGE BY COMBINING DIFFERENTLY EXPOSED PICTURES , 1995 .

[7]  Pascal Fua,et al.  The Radiometry of Multiple Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Takeo Kanade,et al.  Shape from interreflections , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[9]  Takeo Kanade,et al.  Statistical calibration of CCD imaging process , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[10]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[11]  Ronen Basri,et al.  Photometric stereo with general, unknown lighting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[13]  Robert J. Woodham,et al.  Photometric method for determining surface orientation from multiple images , 1980 .

[14]  Takeo Kanade,et al.  Statistical Calibration of the CCD Imaging Process , 2001, ICCV.

[15]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[16]  Berthold K. P. Horn,et al.  Shape from shading , 1989 .

[17]  Shree K. Nayar,et al.  What Can Be Known about the Radiometric Response from Images? , 2002, ECCV.

[18]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[19]  David G. Stork,et al.  Pattern Classification , 1973 .

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

[21]  J. F. Reid,et al.  RGB calibration for color image analysis in machine vision , 1996, IEEE Trans. Image Process..