The problem of estimating spectral reflectances from the responses of a digital camera has received considerable attention recently. This problem can be cast as a regularized regression problem or as a statistical inversion problem. We discuss some previously suggested estimation methods based on critically undersampled RGB measurements and describe some relations between them. We concentrate mainly on those models that are using a priori information in the form of high-resolution measurements. We use the "kernel machine" framework in our evaluations and concentrate on the use of multiple illuminations and on the investigation of the performance of global and locally adapted estimation methods. We also introduce a nonlinear transformation of reflectance values to ensure that the estimated reflection spectra fulfill physically motivated boundary conditions. The reported experimental results are derived from measured and simulated camera responses from the Munsell Matte, NCS, and Pantone data sets.

In a scanning laser microscope detecting fluorescent light from the specimen, the depth-discriminating property of confocal scanning has been used to carry out optical slicing of a thick specimen. The recorded digital images constitute a three-dimensional raster covering a volume of the specimen. The specimen has been visualized in stereo and rotation by making look-through projections of the digital data in different directions. The contrast of the pictures has been enhanced by generating the gradient volume. This permits display of the border surfaces between regions instead of the regions themselves.

Colors are important for human communicating with the daily encountered objects as well as his species, these colors should be represented formally and numerically within a mathematical formula so it can be projected on device/ computer storage and applications, this mathematical representation is known as color model that can hold the color space, by the means of color's primary components (Red, Green, and Blue) the computer can visualizes what the human does in hue and lightness. In this work a review of most popular color models are given (which are RGB, CMY, HSV, and YCbCr) with the explanation of the components, color system, and transformation formula for each other, application areas and usages are also included. Comparison between these different color models is performed by applying Signal to noise Ratio (SNR) metric to indicate the best color models. Results analysis shows the RGB has better results according to SNR measure. General Terms Color Model, RGB, CMY, HSV, YCbCr, skin color detection, segmentation.

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