Simulation of Digital Camera Images from Hyperspectral Input

An important goal for digital color cameras is to record enough information about a scene so that it can be reproduced accurately for a human observer. By accurately, we mean so that the human observer will perceive the reproduction as looking like the original.

[1]  L. Maloney Evaluation of linear models of surface spectral reflectance with small numbers of parameters. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[2]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[3]  Michael H. Brill,et al.  Color appearance models , 1998 .

[4]  Gerald C. Holst,et al.  Sampling, Aliasing, and Data Fidelity for Electronic Imaging Systems, Communications, and Data Acquisition , 1998 .

[5]  Berthold K. P. Horn Exact reproduction of colored images , 1983, Comput. Vis. Graph. Image Process..

[6]  Laurence T. Maloney,et al.  Color constancy and color perception: the linear-models framework , 1993 .

[7]  J. Parkkinen,et al.  Characteristic spectra of Munsell colors , 1989 .

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

[9]  Eero P. Simoncelli Bayesian Denoising of Visual Images in the Wavelet Domain , 1999 .

[10]  Ts Troscianko Hyper-spectral camera system: acquisition and analysis , 1995 .

[11]  T Troscianko,et al.  Color and luminance information in natural scenes. , 1998, Journal of the Optical Society of America. A, Optics, image science, and vision.

[12]  Brian A. Wandell,et al.  A spatial extension of CIELAB for digital color‐image reproduction , 1997 .

[13]  Donald Williams,et al.  Image capture simulation using an accurate and realistic lens model , 1999, Electronic Imaging.

[14]  J. Cohen Dependency of the spectral reflectance curves of the Munsell color chips , 1964 .

[15]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[16]  I. G. Priest THE OPTICAL SOCIETY OF AMERICA. , 1922, Science.

[17]  K. S. Gibson,et al.  Tristimulus Specification of the Munsell Book of Color from Spectrophotometric Measurements , 1943 .

[18]  B. Wandell Foundations of vision , 1995 .

[19]  G. Buchsbaum A spatial processor model for object colour perception , 1980 .

[20]  M. Webster,et al.  Adaptation and the color statistics of natural images , 1997, Vision Research.

[21]  Graham D. Finlayson,et al.  Color by Correlation , 1997, CIC.

[22]  L. Maloney Physics-based approaches to modeling surface color perception , 1999 .

[23]  David H. Brainard,et al.  Reconstructing Images from Trichromatic Samples: From Basic Research to Practical Applications , 1995, CIC.

[24]  D H Brainard,et al.  Bayesian color constancy. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

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

[26]  Brian A. Wandell,et al.  Comparative analysis of color architectures for image sensors , 1999, Electronic Imaging.

[27]  David H. Brainard,et al.  Calibration of a computer controlled color monitor , 1989 .

[28]  D. B. Judd,et al.  Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature , 1964 .

[29]  Gaurav Sharma,et al.  Characterization of Scanner Sensitivity , 1993, Color Imaging Conference.

[30]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.