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Chapter 4. Color Image Processing 1. Color Fundamentals 2. Color Models 3. Basics of Full-Color Image Processing 4. Color Transformations 5. Color Image Smoothing and Sharpening Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 2
1. Color Fundamentals ❖Color spectrum Color spectrum seen by passing white light through a prism. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 3
1. Color Fundamentals ❖Wavelengths Wavelengths comprising the visible range of the electromagnetic spectrum. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 4
1. Color Fundamentals ❖The absorption of light by the red, green, and blue cones in the eye Absorption of light by the red, green, and blue cones in the human eye as a function of wavelength. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 5
1. Color Fundamentals ❖Primary and secondary colors of light and pigments. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 6
1. Color Fundamentals ❖Hue, Saturation, and Brightness ➢ The characteristics generally used to distinguish one color from another are brightness, hue, and saturation. ➢ Brightness embodies the achromatic notion of intensity. ➢ Hue is an attribute associated with the dominant wavelength in a mixture of light waves. Hue represents dominant color as perceived by an observer. ➢ Saturation refers to the relative purity or the amount of white light mixed with a hue. The pure spectrum colors are fully saturated. ➢ Hue and saturation taken together are called chromaticity. → a color may be characterized by its brightness and chromaticity Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 7
1. Color Fundamentals ❖Hue, Saturation, and Brightness ➢ The amounts of red, green, and blue needed to form any particular color are called the tristimulus values, and are denoted, X, Y, and Z, respectively. ➢ A color is then specified by its trichromatic coefficients, Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 8
1. Color Fundamentals ❖CIE chromaticity diagram CIE chromaticity diagram, which shows color composition as a function of x (red) and y (green). For any value of x and y, the corresponding value of z (blue) is obtained from Eq. (7-4) by noting that z = 1 – (x + y). The point marked green in Fig. 7.5 , for example, has approximately 62% green and 25% red content. It follows from Eq. (7-4) that the composition of blue is approximately 13%. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 9
1. Color Fundamentals ❖Color gamut Illustrative color gamut of color monitors (triangle) and color printing devices (shaded region). Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 10
Chapter 4. Color Image Processing 1. Color Fundamentals 2. Color Models 3. Pseudocolor Image Processing 4. Basics of Full-Color Image Processing 5. Color Transformations 6. Color Image Smoothing and Sharpening Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 11
2. Color Models ❖Color model ➢ The purpose of a color model (also called a color space or color system) is to facilitate the specification of colors in some standard way. ➢ A color model is a specification of (1) a coordinate system, and (2) a subspace within that system, such that each color in the model is represented by a single point contained in that subspace. ➢ RGB (red, green, blue) model: color monitors and a broad class of color video cameras ➢ CMY (cyan, magenta, yellow) and CMYK (cyan, magenta, yellow, black) models: printing ➢ HSI (hue, saturation, intensity) model: closely with the way humans describe and interpret color Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 12
2. Color Models ❖The RGB Color Model Schematic of the RGB color cube. Points along the main diagonal have gray values, from black at the origin to white at point (1, 1, 1). Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 13
2. Color Models ❖The RGB Color Model ➢ When fed into an RGB monitor, these three images combine on the screen to produce a composite color image. ➢ The number of bits used to represent each pixel in RGB space is called the pixel depth. ➢ Each RGB color pixel has a depth of 24 bits. ➢ The term full-color image is used often to denote a 24-bit RGB color image. ➢ The total number of possible colors in a 24-bit RGB image is (28)3= 16,777,216. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 14
2. Color Models ❖The RGB Color Model ➢ EXAMPLE: Generating a cross-section of the RGB color cube and its thee hidden planes. (a) Generating the RGB image of the cross-sectional color plane (127, G, B). (b) The three hidden surface Fig. 1. A 24-bit RGB color cube. planes in the color cube of Fig. 1. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 15
2. Color Models ❖The CMY and CMYK Color Models ➢ cyan, magenta, and yellow are the secondary colors of light or, alternatively, they are the primary colors of pigments where the assumption is that all RGB color values have been normalized to the range [0, 1]. ➢ CMY to CMYK: ▪ Let: ▪ If K = 1 then we have pure black, with no color contributions Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 16
2. Color Models ❖The CMY and CMYK Color Models ➢ CMY to CMYK: ➢ CMYK to CMY: Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 17
2. Color Models ❖The HSI Color Model Conceptual relationships between the RGB and HSI color models. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 18
2. Color Models ❖The HSI Color Model Hue and saturation in the HSI color model. The dot is any color point. The angle from the red axis gives the hue. The length of the vector is the saturation. The intensity of all colors in any of these planes is given by the position of the plane on the vertical intensity axis. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 19
2. Color Models ❖The HSI Color Model Color detection The HSI color model based on (a) triangular, and (b) circular color planes. The triangles and circles are perpendicular to the vertical intensity axis. Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 20
2. Color Models ❖The HSI Color Model ➢ Converting Colors from RGB to HSI Rafael C. Gonzalez, Richard E. Woods, “Digital image processing,” Pearson (2018). 21
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