CH 6 Color Image Processing

CH 6 Color Image Processing 6.1 Color Fundamental 6.2 Color models 6.3 Pseudo color processing 6.4 Basics of full-color image processing 6.5 Color transformation 6.6 Smoothing and sharpening

6.1 Color fundamental • Issac Newton, 1666

Visible light • Chromatic lightspan the electromagnetic spectrum (EM) from about 400 to 700 nm

Physical quantities to describe a chromatic light source • Radiance: total amount of energy that flow from the light source, measured in watts (W) • Luminance: amount of energy an observer perceivesfrom a light source, measured in lumens (lm) • Far infrared light: high radiance, but 0 luminance • Brightness: subjective descriptor that is hard to measure, similar to the chromatic notion of intensity

Primary and secondary colors • In 1931, CIE(International Commission on Illumination) defines specific wavelength values to • the primary colors • B = 435.8 nm, G = 546.1 nm, R = 700 nm • However, we know that no single colormay be called red, green, or blue • Secondary colors: • G+B=Cyan, R+G=Yellow, R+B=Magenta

Primary and secondary colors

How human eyes sense light? • 6~7M cones are the color sensors in the eye • 3 principal sensing categories in eyes • 65% of all cones are sensitive to Red light • 33% to Green light • 2% to Blue light • Absorption of light (available in1965)

CIE XYZ model • RGB -> CIE XYZ model • Normalized tristimulus values => x+y+z=1. Thus, x, y (chromaticity coordinate) is enough to describe all colors  chromaticity diagram

CIE chromaticity diagram • Color composition as x(red) & y(green) • E.g. + marked point Green: 62% Red: 25% Blue: 13%

Typical color gamut of monitors and printers Color monitors Color printers

6.2 Color models (Color space or color system) • Purpose : to facilitate the specification of colors in some standard • RGB models: color monitors • CMY(CMYK): color model for color printing • YIQ or YUV: color model for color television • HSI: a color model for humans to describe and to interpret color; decouple the color and gray-level information

Chapter 6 Color Image Processing • RGB Color model • - Have a depth of 24bits (3 x 8 bit)

Chapter 6 Color Image Processing • CMY color model • The YIQ model

Chapter 6 Color Image Processing • HSI color model • Hue: a color attributes associated with dominant wavelength • Saturation : a measure of the degree to which a pure color is diluted by a color • Intensity (gray-level): most useful descriptor of monochromatic images. Decoupled from color carry components • - HIS is an ideal tool for developing image processing algorithm

Chapter 6 Color Image Processing

Converting colors RGB  HSI

Converting colors from HIS to RGB, when given values of HIS [0,1] -> multiply H by 360o; • (1) When H is in RG sector(0 H 120), B=I(1-S) … • (2) H is in GB sector (120 H 240): H= H-120, R=I(1-S)… • (3) H is in BR sector (240 H 360): H= H-240, G=I(1-S)…

6.3 Pseudo color image processing6.3.1 Intensity slicing • If an image is interpreted as a 3-D function, the method can be viewed as one of placing planes parallel to the coordinate plane of the image • Each plane ‘slices’ the function in the area of intersection

Two-color image whose relative appearance can be controlled by moving the slicing plane up and down the gray-level axis • Gray-level to color assignments • f(x,y)= ck, if f(x,y) Vk • (ck=color , Vk= kth partitioning plane)

The gray scale was divided into intervals and a different color was assigned to each region • simple but powerful aid in visualization, especially if numerous images are involved

6.3.2 Gray level to color transformation • Achieve a wide range of pseudo color enhancement • Perform 3 independent transformations on the gray levels of any input pixels;

Chapter 6 Color Image Processing • Combine several monochrome images into a single color composite

Chapter 6 Color Image Processing • Used for multi-spectral image processing(different sensors produces individual monochrome images) • Difference in color in various parts of the Potomac River

6.4 Basics of full color image processing • Two categories: • (1) Process each component individually and then form a composite processed image • (2) Work with color pixels directly • Color pixel are vectors • Let C be an arbitrary vector • Two conditions must be satisfied for pre-color-component and vector-based processing • The process has to be applicable to both vectors and scalars • The operator must be independent of the other components

6.5 Color transformations 6.5.1 Formulation of color transformation • g(x,y)=T[f(x,y)] • Color transformation (color mapping) of the form • si=Ti [r1 , r2,,…rn](n: number of components) • E.g.: modify the intensity of the image on Fig. 6.30(a) using g(x,y)=k*f(x,y) • In HSI color space, • this can be done with H: s1=r1, S: s2=r2, I: s3=k*r3 (Only intensity component must be transformed)

In RGB space, this can be done with three components si=k*ri(i=1,2,3.) • In CMYK space, it requires a set of linear transformations • si=k*ri+ (1-k) (i=1,2,3.)

6.5.2 Color complements • - Hue directly opposite one another on the color circle (analogous to the gray-level negatives) • - Useful for enhancing details that is embedded in dark region of a color image

6.5.3 Color slicing • Highlighting a specific range of colors for separating objects: two approaches (1) To display the colors of interest (2) To use the region defined by the colors as a mask for further processing • ‘slice’ a color image: mapping the colors outside some range of interest to a non-prominent neutral color (0.5-gray)

Method 1: enclosed by a cube (a cube with width W and centered at a prototypical color with components) • Highlight the colors around the prototype by forcing all other colors to the midpoint of the reference color space (0.5-gray) • Method2:enclosed by a sphere

6.5.4 Tone and color correction • Tonal range of an image (Key type)-refers to its general distribution of color intensities • High-key image is concentrated at high intensities • Low-key image are located at low intensities • It is desirable to distribute the intensities of a color image equally between the highlight and shadow area •  Modifying tones normally are selected interactively • Operation : adjust the images’ brightness and contrast over a suitable range of intensities • In RGB and CMYK: map all three components • In HIS : only intensity component is modified

Color imbalance—analyzing with color spectrometer • color wheel can be used to predict how one component will affect another • The perception of one color is affected by its surrounding colors • The portions of any color can be increased by decreasing the amount of the opposite • Transformation functions required for correcting the images (Fig. 6.36)

6.5.5 Histogram processing • The gray-level processing can be applied to color images in an automatic way • Produce an image with a uniform histogram of intensity values • To histogram equalize the components of a color image individually is unwise (results in erroneous color) • A more logical approach: spread the color intensities uniformly (use HSI space),leaving the colors themselves unchanged

CH 6 Color Image Processing