數(shù)字圖像處理,岡薩雷斯,課件英文版Chapter06彩色圖像處理

《數(shù)字圖像處理,岡薩雷斯,課件英文版Chapter06彩色圖像處理》由會(huì)員分享，可在線閱讀，更多相關(guān)《數(shù)字圖像處理,岡薩雷斯,課件英文版Chapter06彩色圖像處理（106頁(yè)珍藏版）》請(qǐng)?jiān)谘b配圖網(wǎng)上搜索。

1、Digital Image Processmgi Chapter 6: Color Image Processing 6 July 2005 Spectrum of White Light FIGURE 6.1 Color spectrum seen by passing white light through a prisni.(Coiirtesy of the General Electric CoM Lamp Business Division.) 1666 Sir Isaac Newton, 24 year old, discovered white light

2、spectrum. Electromagnetic Spectrum X RATS 1 rr !CTO- WAVES 001?ifn ULTRAVIOLET 3DO GAMMA RAYS VISieiE SPECTRUM NFRARED iaoo 1iOO 400 500 600 700 WAVELENGTH RADIO .0001 ft J Visible light wavelength: from around 400 to 700 nm 1. For an achromatic (monochrome) light source, the

3、re is only 1 attribute to describe the quality: intensity 2. For a chromatic light source, there are 3 attributes to describe the quality: Radiance 二 total amount of energy flow from a light source (Watts) Luminance 二 amount of energy received by an observer (lumens) Brightness 二 intensity Sensi

4、tivity of Cones in the Human Eye (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Blue Green Red 6-7 millions cones in a human eye -65% sensi

5、tive to Red light -33% sensitive to Green light -2 % sensitive to Blue light (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd

6、Edition. 002dmd=ffi 4 o 45 o 60 ^050 U3CD」D H w mo p 7oo nm Primary colors: 鼓 Defined CIE in 1931 Red 二 700 nm Green 二 546. lnm Blue = 435.8 nm CIE = Commission Internationale de PEclairage (The International Commission on Illumination) (Images from Rafael C? Gonzalez and Richard E

7、. Wood, Digital Image Processing. 2nd Edition. Primary and Secondary Colors Primary and Secondary Colors Primary color Secondary 歹 colors Primary color Primary color Primary and Secondary Colors (cont) PRIMARY AND SECOND

8、ARY COLORS OF LIGHT AND PIGMENT Additive primary colors: RGB use in the case of light sources such as color monitors RGB add together to get white Subtractive primary colors: CMY use in the case of pigments in printing devices White subtracted by CMY to get Black Color Characterization Hue:

9、Saturation: Brightness: Hue Saturation dominant color corresponding to a dominant wavelength of mixture light wave Relative purity or amount of white light mixed with a hue (inversely proportional to amount of white light added) Intensity } Chromaticity amount of red (X), green (Y) and blue

10、(Z) to form any particular color is called tristimulus. CIE Chromaticity Diagram X Trichromatic coefficients: X Y — ~ X+Y + Z Y y = X+Y + Z Z Z~ X+Y + Z x + y + z = l Points on the boundary are fully saturated colors (Images from Rafael C? Gonzalez and Richard E. Wood, Dig

11、ital Image Processing. 2nd Edition. Color Gamut of Color Monitors and Printing Devices x-axis ? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. RGB Color Cube Purpose of color models: to facilitate the specification of colors in some standard B RGB color model

12、s: -based on cartesian coordinate system R = 8 bits G = 8 bits B = 8 bits Color depth 24 bits =16777216 colors (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. RGB Color Cube (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Im

13、age Processing. 2nd Edition. RGB Color Cube (R = o) (G = 0) (B = 0) Hidden faces of the cube (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. RGB Color Model (coni) (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Ima

14、ge Processing. 2nd Edition. RGB Color Model (coni) Red fixed at 127 LJ Blue (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Safe RGB Colors Safe RGB colors: a subse

15、t of RGB colors. There are 216 colors common in most operating systems. a b FIGURE 6.10 (a) The 216 safe RGB colors. (b) All the grays in the 256-color RGB system r (grays that are part of the safe color group are shown underlined)? Safe RGB Colors Safe RGB Colors LLU1L 負(fù)謖 u

16、 三三 oslclsl uuuuy H0SSH 666666 LUdmUJ山MJ ■■■■■□□□□□□□□□□□ (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. RGB Safe-color Cube Number Svstcm ■ Hex 00 Decimal 0 TABLE 6 A Valid values of each RG B component in a safe colo

17、r. Color Equivalents 33 66 99 CC FF 51 102 153 204 255 The RGB Cube is divided into 6 intervals on each axis to achieve the total 63= 216 common colors? However, for 8 bit color representation, there are the total 256 colors. Therefore, the remaining 40 colors are left to OS. (Images from R

18、afael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. CMY and CMYK Color Models C = Cyan M = Magenta Y 二 Yellow K 二 Black CMY and CMYK Color Models CMY and CMYK Color Models c 1 R M — 1 — G Y 1 B Relationship Betwe

19、en RGB and HSI Color Models Color carrying information RGB, CMY models are not good for human interpreting HSI Color model: Hue: Dominant color Saturation: Relative purity (inversely proportional to amount of white light added) Intensity: Brightness RGB HSI (Images from Rafael C? Go

20、nzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. HSI Color Model (cont.) 1. A dot is the plane is an arbitrary color 2. Hue is an angle from a red axis. 3. Saturation is a distance to the point. Intensity is given by a position on the vertical axis. HSI Color Mo

21、del Intensity is given by a position on the vertical axis. Example: HSI Components of RGB Cube (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Editio

22、n. RGB Cube Hue Saturation Intensity (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Converting Colors from RGB to HSI H=\e 360 — 0 if BG

23、 0 = cos-1 *[(—G) + (R —B)] (R - GY + (R — B)(G - = 1- I = ^R + G + B) Converting Colors from HSI to RGB RG sector: 0

24、R + G) BR sector: 240

25、 from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Processing There are 2 types of color image processes 1. Pseudocolor image process: Assigning colors to gray values based on a specific criterion. Gray scale images to be processed may be a singl

26、e image or multiple images such as multispectral images Full color image process: The process to manipulate real color images such as color photographs? Pseudocolor Image Processing Pseudo color 二 false color : In some case there is no "color" concept for a gray scale image but we can assign

27、"false" colors to an image. Why we need to assign colors to gray scale image? Answer: Human can distinguish different colors better than different shades of gray. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Intensity Slicing Example (Im

28、ages from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Intensity Slicing Example Formula: (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Intensity Slicing Example g（兀 y）= C7 = Color No. 1 C2 = Color N

29、o. 2 o o G Q I I I I o L-l Intensity An X-ray image of a weld with cracks After assigning a yellow color to pixels with value 255 and a blue color to all other pixels. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Multi Level Intensi

30、ty Slicing g(x, y) = Ck for lk_x < f(x, y) < lk Multi Level Intensity Slicing Example (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Multi Level Intensity Slicing Example g{x.y) = Ck forZ^ Ck = Color No. k lk = Threshold level k A

31、n X-ray image of the Picker After density slicing into 8 colors Thyroid Phanto m. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Coding Example Color coded i

32、mage Color map South America region A unique color is assigned to each intensity value? < Gray-scale image of average monthly rainfall. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Gray Level to Color Transformation Example Assigning

33、colors to gray levels based on specific mapping functions Red component Green component Blue component  Transformations Color coded images (Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. An X-ray image of a garment bag / An X-ray image of a

34、garment bag with a simulated explosive Gray Level to Color Transformation Example Gray Level to Color Transformation Example 二一 i丄 L - irccn images -1丄 Blue Color coded An X-ray image of a garment bag An X-ray image of a garment bag with a simulated explosive device ? (jrav

35、lcvcl 1 ? F;vnlHivp Ci nrmpnI 11 nrk erm rod Transformations (Images from Rafael C? Gonzalez and Richard E. Wood. Digital Image Processing, 2nd Edition. Pseudocolor Coding Used in the case where there are many m

36、onochrome images such as multispectral satellite images? (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Pseudocolor Coding Example Visible blue X= 0.45-0.52 ym Visible green %= 0.52-0.60 ]im Measuring plant Max water penetration :I . p ” C

37、olor composite images Green =② Blue = ? (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Pseudocolor Coding Example Washington D.C. area Visible red Near infrared X= 0.63-

38、0.69 pim X= 0.76-0.90 ym Plant discrimination Biomass and shoreline mapping (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Pseudocolor Coding Example A close-up Psuedocolor rendition of Jupiter moon Io Yellow areas 二 older sulfur deposits. R

39、ed areas 二 material ejected from active volcanoes? (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing, 2nd Edition. Basics of Full-Color Image Processing 2 Methods: 1. Per-color-component processing: process each component separately. 2. Vector processing: treat

40、each pixel as a vector to be processed? Example of per-color-component processing: smoothing an image Spatial mask Gray-scale image By smoothing each RGB component separately. Example： Full-Color Image and Variouis Color Space Components Full color Color image Black Yellow CMYK components

41、 Magenta RGB components Hue Saturation Intensity HSI components (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Example: Color Transformation Use to transform colors to colors? Formulation: g(x,y) = T[f(x,y)] f(x,y) = input color image,

42、= output color image T = operation on/over a spatial neighborhood of (xj) When only data at one pixel is used in the transformation, we can express the transformation as: Si = TU，叮 i= 1,2, ...,n Where 々二 color component of f(x,y) For RGB images, n = 3 Sj = color component of g(x,y) Formula f

43、or HSI: y) = kr}(x, y) Formula for CMY: — Z / / E H3 Iqm,y| Formula for RGB: sR{x,y) = krR{x,y) sG(x,y) = k^(x.y) sB{x,y) = krB{x,y) (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Exam

44、ple: Color Transformation (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Example: Color Transformation sc(x, y) = krc^x, y) + (1 - k) sM(x,y) = kr^(x.y) + (l-k) sY(x,y) = k^(x,y) + (l-k) These 3 transformations give the same results? (I

45、mages from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Complement Transformation Example Color complement replaces each color with its opposite color in the color circle of the Hue component. This operation is analogous to image negative in a gray scal

46、e image. Cyari J Color circle  0 1 0 / / / / H | 1 a b c d FIGURE 6.33 Color complcinent transform alionsu (a) Original image. (b) Complement transformation functions. (c) Comp lenient of (a) based on the RGB mapping functions, (cl) An approximation of t

47、he RGB complement usiim HSI J Ininsformalionsu (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Slicing Transformation w 0.5 if rj ~ aJ > — 2_ r otherwise K * s：= We can perform "slicing in color space: if the color of eac

48、h pixel is far from a desired color more than threshold distance, we set that color to some specific color such as gray, otherwise we keep the original color unchanged? i Set to gray any 1< j

49、riginal color Color Slicing Transformation Example (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Slicing Transformation Example After color slicing Original image a h FIGURE 6.34 Color slicing transformations that detect

50、(a) reds within an RGB cube of width W = 0.2549 centered at (0.6863, 0.1608, 0.1922), and (b) reds within an RGB sphere of radius 0.1765 centered at the same point. Pixels outside the cube and sphere were replaced by color (0.5,0.5? 0.5)?

51、 (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Tonal Correction Examples (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image P

52、rocessing. 2nd Edition. Dark Coi reeled 0 / / 1 KAR | In these examples, only brightness and contrast are adjusted while keeping color unchanged? This can be done by using the same transformation for all RGB components ? 0 Contrast enhancement Power law tr

53、ansformations (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Original/Cbrrecled Color Balancing Correction Examples FIGURE 6.36 Color kilaix;ing corrcclioiib for CM

54、YK color images. Color imbalance: primary color components in white area are not balance? We can measure these components by using a color spectrometer. Color balancing can be performed by adjusting color components separately as seen in this slide. (Images from Rafael C? Gonzalez and Richard E.

55、 Wood, Digital Image Processing. 2nd Edition. Heavy in magenta ■ a ■ Rai m 0 1 (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Histogram Equalization of a Full-Color Image ? Histog

56、ram equalization of a color image can be performed by adjusting color intensity uniformly while leaving color unchanged. ? The HSI model is suitable for histogram equalization where only Intensity (I) component is equalized. k M where r and s are intensity components of input and output col

57、or image. Histogram Equalization of a Full-Color Image (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Histogram Equalization of a Full-Color Image 0 1 FIGURE 6.37 Hislogram eqikilization (followed by satu ration adjustment) in lhe HSI

58、 color space? (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Smoothing 2 Methods: 1. Per-color-plane method: for RGB, CMY color models Smooth each color plane

59、 using moving averaging and the combine back to RGB 亍工R(x,y) K (兀,)冶小 c(x, y) = — 工 cO,y) = K (x,y)eS^ 工 G(x,y) K (x,y)wS, ~工b(兀,y) K (兀,)‘)wSg 2. Smooth only Intensity component of a HSI image while leaving H and S unmodified? Note: 2 methods are not equivalent. Color Image Smooth

60、ing Example (cont) (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Smoothing Example (cont) Color image Green (Images from Rafael C? Gonzalez and Richard

61、E. Wood, Digital Image Processing. 2nd Edition. Color Image Smoothing Example (cont) Color Image Smoothing Example (cont) Color image HSI Components Tl s Intensity Wood, Digital Image Processing, 2nd Edition.

62、 Color Image Smoothing Example (cont) (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Smoothing Example (cont) Smooth all RGB components Smooth only I component of HSI (faster)

63、 (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Smoothing Example (cont) (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Smoothing Exampl

64、e (cont) Difference between smoothed results from 2 methods in the previous slide. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Image Sharpening We can do in the same manner as color image smoothing: 1. Per-color-plane method for RGB,C

65、MY images 2. Sharpening only I component of a HSI image Sharpening all RGB components Sharpening only I component of HSI Color Segmentation Difference between sharpened results from 2 methods in the previous slide. 2 Methods: 1. Segmented in HSI color space: A thresholding function base

66、d on color information in H and S Components? We rarely use I component for color image segmentatio n. 2. Segmentation in RGB vector space: A thresholding function based on distance in a color vector space. (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Segmentation in HSI Color Space (Images from Rafael C? Gonzalez and Richard E. Wood, Digital Image Processing. 2nd Edition. Color Segmentation in HSI Color Spa