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Digital Image Processing. Sheng-Fang Huang Chapter 6. 6.1 Color Fundamentals. White light consists of a continuous spectrum of colors ranging from violet to red. Color Spectrum. Visible light is composed of a relatively narrow band of frequencies in the electromagnetic spectrum.
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Digital Image Processing Sheng-Fang Huang Chapter 6
6.1 Color Fundamentals White light consists of a continuous spectrum of colors ranging from violet to red.
Color Spectrum Visible light is composed of a relatively narrow band of frequencies in the electromagnetic spectrum.
Color Fundamentals • The colors that humans perceive of an object are determined by the nature of the light reflected from the object. • Green objects reflect light with wavelengths in the 500 to 570 nm, and absorb those at other wavelengths. • The light is visible to human eyes if its wavelength is between 380-780 (nm). • If the light is achromatic, its only attribute is intensity. • The term gray level refers to a scalar ranging from black to white.
Color Fundamentals • The cone cells in human eye can be divided into three categories, corresponding roughly to red, green and blue (Figure 6.3). • Due to these characteristics of the human eye, colors are seen as variable combinations of the primary colors red (700 nm), green (546.1 nm), and blue (435.8 nm). • Standardized in 1931. • This standardization does not mean these three primary colors can generate all spectrum colors.
Secondary Colors • The primary colors can be added to produce the secondary colors of light: Cyan (青綠), Magenta (洋紅)，Yellow (黃). • The primary colors of pigments are cyan, magenta, and yellow, while the secondary colors are red, green, and blue.
Color Fundamentals • The characteristics generally used to distinguish one color from another are hue, saturation, and brightness. • Hue: associated with color as perceived by an observer. • Saturation: relative purity or the amount of white light mixed with a hue. • Brightness: intensity of light. • Hue and saturation are taken together are called chromaticity; therefore, a color can be charaterized by its chromaticity and brightness.
6.2 Color Models • The color model (color space or color system) is to facilitate the specification of colors in some standards. • color model is a specification of a coordinate system and a subspace within the system where a color is represented. • RGB: color monitor • CMY (cyan, magenta, yellow): color printing • HSI (hue intensity and saturation): decouple the color and gray-scale information.
The RGB Color Model Images represented in the RGB color model consist of three component images, one for each primary image.
The RGB Color Model The number of bits used to represent each pixel in RGB space is called the pixel depth. The term full-color image is used to denote a 24-bit RGB color image.
The CMY Color Model • Suppose colors in RGB are normalized in [0, 1]. The RGB to CMY conversion is given by • Instead of adding C,M, and Y to produce black, a fourth color black is added, giving rise to the CMYK color model.
The HSI Color Model • Human describes color in terms of hue, saturation and brightness. • Hue: describe the pure color, pure yellow, orange, green or red. • Saturation measures the degree to which a pure color is diluted by white light. • Brightness is a subjective descriptor difficult to be measured. • Comparison: • The RGB model is ideal for image color generation. • The HSI model is an ideal tool for developing image processing algorithms based on color descriptions.
How to Convert RGB to HSI • Consider a color point in the RGB color cube. • Intensity: find the intersection on the intensity axis with a perpendicular plane containing the color point. • Saturation: The distance of the color point to the intensity axis. • The saturation on the intensity axis is zero. • Hue: consider the triangle enclosed by white, black, cyan. The color on this triangle is a mixture of these three colors.
The HSI Color Model All pointes contained in the plane segment are defined by the intensity and boundary of the cube have the same hue
Converting Colors • From RGB to HSI • S=1-[3/(R+G+B)][min(R, G, B)] • I=(R+G+B)/3
Converting colors • RG sector (0<H<120) B = I(1-S) G = 3I-(R+B)
Converting colors • GB sector (120≤H<240) • First, let H = H -120 R=I(1-S) B=3I-(R+G)
Converting colors • BR sector (240 ≤H ≤ 360) • First, let H = H -240 G = I(1-S) R = 3I-(G+B)
6.3 Pseudo Image Processing • Assigning colors to gray values based on a specified criterion. • Intensity slicing: using a plane at f(x, y)=li to slice the image function into two levels. • We assume that P planes perpendicular to the intensity axis defined at level li i=1,2,..P. These P planes partition the gray level in to P+1 intervals: Vk k=1,2,..P+1 • f(x, y)=ci if f(x, y) Vk where ciis the color associated with the kth intensity interval Vkdefined by the partition lanes at l=k-1 and l=k.
Gray Level to Color Transformation • Three independent transformation functions on the gray-level of each pixel. • This method produces a composite image whose color content is modulated by the nature of the transformation functions.
6.3 Pseudo Image Processing • Combine several monochrome images into a single color image
Example 6.6 One way to combine the sensed image data is by how they show either differences in surface chemical composition or changes in thee way the surface reflects sunlight.
6.4 Full-Color Image Processing • Two categories: • Process each component individually and then form a composite processed color image from the components. • Work with color pixels directly. In RGB system, each color point can be interpreted as a vector. • c(x, y)=[cR(x, y), cG(x, y), cB(x, y)]
6.5 Color Transformation • Formulation Gray-level transformation g(x, y)=T[f(x, y)] Color transformation si=Ti (r1, r2,….rn) I =1,2,…, n where ri and si are variables denoting the color component of f(x, y) and g(x, y) at any point (x, y), n is the number of color components, and {Ti} is a set of transformation or color mapping functions
Color Transformation • To modify the intensity of the image g(x,y)=kf(x,y) 0<k<1 • HSI : s3=kr3 • RGB: si=kri i=1, 2, 3 • CMY: si=kri+(1-k) i=1, 2, 3
6.5.2 Color Complements • The hues directly opposite one another on the color circle are called complements • Color complements are useful for enhancing detail that is embedded in dark regions of a color image
Example 6.7 Unlike Fig. 6.31, the RGB complement transformation functions used in this example do not have a straightforward HSI space equivalent, because the saturation component of the complement cannot be computed from the saturation component alone.
6.5.3 Color Slicing • Highlighting a specific range of colors in an image is useful for separating object from their surrounding. • The simplest way to “slice” a color image is to map the colors outside some range of interest to a nonprominent neutral color (e.g., (R, G, B)=(0.5, 0.5, 0.5)). • If the colors of interest are enclosed by a cube (or hypercube for n>3) of width W and centered at a average color with component (a1, a2,…an) the necessary set of transformation is
6.5 Color Transformation - Color Slicing • If a sphere is used to specify the colors of interest then • Forcing all other colors to the mid point of the reference color space. • In RGB color space, the neural color is (0.5, 0.5, 0.5)
6.5.4 Tone and Color Correction • In the RGB, the transformation is achieved by mapping all three (or four) color components with the same transformation function. • In the HSI color space, only the intensity component is modified.
Example 6.9 Color Transformation Tonal transformation for flat, light and dark images
Example 6.10 Color Correction Color Balancing: The proportion of any color can be increased by decreasing the amount of opposite (complementary) color in the image.
6.5.5 Histogram Processing • Equalizing the histogram of each component will result in erroneous colors. • Spread the color intensity uniformly, leaving the color themselves (hues) unchanged. • Equalizing the intensity histogram affects the relative appearance of colors in an image.
