Digital Image Processing Chapter 6: Color Image Processing

Transcription

1 Digital Image Processing Chapter 6: Color Image Processing

2 Spectrum of White Light 1666 Sir Isaac Newton, 24 ear old, discovered white light spectrum.

3 Electromagnetic Spectrum Visible light wavelength: from around 400 to 700 nm 1. For an achromatic monochrome light source, there is onl 1 attribute to describe the qualit: intensit 2. For a chromatic light source, there are 3 attributes to describe the qualit: Radiance = total amount of energ flow from a light source Watts Luminance = amount of energ received b an observer lumens Brightness = intensit

4 Sensitivit of Cones in the Human Ee 6-7 millions cones in a human ee - 65% sensitive to Red light - 33% sensitive to Green light - 2 % sensitive to Blue light Primar colors: Defined CIE in 1931 Red = 700 nm Green = 546.1nm Blue = nm CIE = Commission i Internationale de l Eclairage l The International Commission on Illumination

5 Primar and Secondar Colors Primar color Secondar colors Primar color Primar color

6 Primar and Secondar Colors cont. Additive primar colors: RGB use in the case of light sources such as color monitors RGB add together to get white Subtractive primar colors: CMY use in the case of pigments in printing devices White subtracted t b CMY to get Black

7 Color Characterization Hue: dominant color corresponding to a dominant wavelength of miture light wave Saturation: Rl Relative purit or amount of white light lihmied with a hue inversel proportional to amount of white light added Brightness: Intensit Hue Saturation Chromaticit amount of red X, green Y and blue Z to form an particular ou o ed, g ee d b ue o o p cu color is called tristimulus.

8 CIE Chromaticit Diagram Trichromatic ih i coefficients: ffii X X Y Z X Y Y Z z X Z Y Z z 1 Points on the boundar are full saturated colors

9 Color Gamut of Color Monitors and Printing Devices Color Monitors Printing devices

10 RGB Color Model Purpose of color models: to facilitate the specification of colors in some standard RGB color models: - based on cartesian coordinate sstem

11 RGB Color Cube R = 8 bits G = 8 bits B = 8 bits Color depth 24 bits = colors Hidden faces of the cube

12 RGB Color Model cont. Red fied at 127

13 Safe RGB Colors Safe RGB colors: a subset of RGB colors. There are 216 colors common in most operating sstems.

14 RGB Safe-color Cube The RGB Cube is divided into 6 intervals on each ais to achieve the total 6 3 = 216 common colors. However, for 8 bit color representation, there are the total 256 colors. Therefore, the remaining 40 colors are left to OS.

15 CMY and CMYK Color Models CMY and CMYK Color Models C = Can M Magenta M = Magenta Y = Yellow K = Black K Black G R M C 1 1 B Y 1

16 HSI Color Model RGB, CMY models are not good for human interpreting HSI Color model: Hue: Dominant color Saturation: ti Relative purit inversel proportional to amount of white light added Color carring if informationi Intensit: Brightness

17 Relationship Between RGB and HSI Color Models RGB HSI

18 Hue and Saturation on Color Planes 1. A dot is the plane is an arbitrar color 2. Hue is an angle from a red ais. 3. Saturation is a distance to the point.

19 HSI Color Model cont. Intensit is given b a position on the vertical ais.

20 HSI Color Model Intensit is given b a position on the vertical ais.

21 Eample: HSI Components of RGB Cube RGB Cube Hue Saturation Intensit

22 Converting Colors from RGB to HSI Converting Colors from RGB to HSI G B G B H if 360 if B R G R 2 1/ cos B G B R G R B G R S 3 1 B G R 3 1 B G R I 3

23 Converting Colors from HSI to RGB RG sector: 0 H 120 GB sector: 120 H 240 H H 120 R S cos H I 1 cos60 H R I 1 S B I 1 S S cos H G I 1 cos60 H G 1 R B B 1 R G BR sector: 240 H 360 H H 240 B G I 1 I 1 S S cos H cos60 H R 1 G B

24 Eample: HSI Components of RGB Colors RGB Image Hue St Saturationti It Intensit

25 Eample: Manipulating HSI Components RGB Image Hue Hue Saturation Saturation Intensit Intensit RGB Image

26 Color Image Processing There are 2 tpes of color image processes 1. Pseudocolor image process: Assigning i colors to gra values based on a specific criterion. Gra scale images to be processed ma be a single image or multiple images such as multispectral images 2. Full color image process: The process to manipulate real color images such as color photographs.

27 Pseudocolor Image Processing Pseudo color = false color : In some case there is no color concept for a gra scale image but we can assign false colors to an image. Wh we need to assign colors to gra scale image? Answer: Human can distinguish i i different colors better than different shades of gra.

28 Intensit Slicing or Densit Slicing Formula: g, C C 2 if if f, T f, T 1 C 1 = Color No. 1 C 2 = Color No. 2 T Color C 2 C 1 A gra scale image viewed as a 3D surface. 0 T L-1 Intensit

29 Intensit Slicing Eample An X-ra image of a weld with cracks After assigning a ellow color to piels with value 255 and a blue color to all other piels.

30 Multi Level Intensit Slicing g, Ck for lk 1 f, lk Color C k = Color No. k l k = Threshold level k C k C k-1 C 3 C 2 C 1 0 l 1 l 2 l 3 l k-1 l k Intensit L-1

31 Multi Level Intensit Slicing Eample g, Ck for lk 1 f, l k C k = Color No. k l k = Threshold level k An X-ra image of the Picker Throid Phantom. After densit slicing into 8 colors

32 Color Coding Eample A unique color is assigned to each intensit value. Gra-scale image of average monthl rainfall. Color map Color coded image South America region

33 Gra Level to Color Transformation Assigning colors to gra levels based on specific mapping functions Red component Gra scale image Green component Blue component

34 Gra Level to Color Transformation Eample An X-ra image of a garment bag An X-ra image of a garment bag with a simulated eplosive device Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition. Transformations Color coded images

35 Gra Level to Color Transformation Eample An X-ra image of a garment bag An X-ra image of a garment bag with a simulated eplosive device Transformations Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition. Color coded images

36 Pseudocolor Coding Used in the case where there are man monochrome images such as multispectral satellite images.

37 Pseudocolor Coding Eample Visible blue = m Ma water penetration Visible green = m Measuring plant 1 2 Color composite images 3 4 Red = 1 Red = 1 Green = 2 Green = 2 Blue = 3 Blue = 4 Visible iibl red Near infrared = m = m Plant discrimination Biomass and shoreline mapping Washington D.C. area

38 Pseudocolor Coding Eample Psuedocolor rendition of Jupiter moon Io Yellow areas = older sulfur deposits. Red areas = material ejected from active volcanoes. Aclose close-up

39 Basics of Full-Color Image Processing 2 Methods: 1. Per-color-component processing: process each component separatel. 2. Vector processing: treat each piel as a vector to be processed. Eample of per-color-component processing: smoothing an image B smoothing each RGB component separatel.

40 Eample: Full-Color Image and Variouis Color Space Components Color image CMYK components RGB components HSI components

41 Color Transformation Use to transform colors to colors. Formulation: g, T f, f, = input color image, g, = output color image T = operation on f over a spatial neighborhood of, When onl data at one piel is used in the transformation, we can epress the transformation as: s i Ti r1, r2,, rn i= 1, 2,, n Where r i = color component of f, For RGB images, n = 3 s i = color component of g,

42 Eample: Color Transformation Eample: Color Transformation Form la for RGB:,, k kr s R R Formula for RGB:,,,, kr s kr s B B G G k Formula for HSI: k = 0.7,, kr s I I Formula for CMY: 1,, k kr s C C I H,S 1 1,, 1,, k kr s k kr s k kr s M M C C These 3 transformations give the same results 1,, k kr s Y Y the same results.

43 Color Complements 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 gra scale image. Color circle

44 Color Complement Transformation Eample

45 Color Slicing Transformation We can perform slicing in color space: if the color of each piel is far from a desired color more than threshold distance, we set that color to some specific color such as gra, otherwise we keep the original color unchanged. or s i 0.5 ri if rj a j otherwise W 2 an 1 j n i= 1, 2,, n Set to gra Keep the original color s i 0.5 ri if n j1 r otherwise j a j 2 R 2 0 i= 1, 2,, n Set to gra Keep the original color

46 Color Slicing Transformation Eample After color slicing Original image

47 Tonal Correction Eamples In these eamples, onl brightness and contrast are adjusted while keeping color unchanged. This can be done b using the same transformation for all RGB components. Contrast enhancement Power law transformations

48 Color Balancing Correction Eamples Color imbalance: primar color components in white area are not balance. We can measure these components b using a color spectrometer. Color balancing can be performed b adjusting color components separatel as seen in this slide.

49 Histogram Equalization of a Full-Color Image Histogram equalization of a color image can be performed b adjusting color intensit uniforml while leaving color unchanged. The HSI model is suitable for histogram equalization where onl Intensit I component is equalized. s k T rk pr rj k j0 n j N k j0 where r and s are intensit components of input and output color image.

50 Histogram Equalization of a Full-Color Image Original image After histogram equalization After increasing saturation component Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

51 Color Image Smoothing Color Image Smoothing 2 Methods: 1. Per-color-plane method: for RGB, CMY color models Smooth each color plane using moving averaging and Smooth each color plane using moving averaging and the combine back to RGB S R K,, 1 S S G K K,,, 1, 1, c c S B K,, 1 2. Smooth onl Intensit component of a HSI image while leaving H ds difi d H and S unmodified. Note: 2 methods are not equivalent.

52 Color Image Smoothing Eample cont. Color image Red Green Blue

53 Color Image Smoothing Eample cont. Color image HSI Components Hue Saturation Intensit

54 Color Image Smoothing Eample cont. Smooth all RGB components Smooth onl I component of HSI faster

55 Color Image Smoothing Eample cont. Difference between smoothed results from 2 methods in the previous slide.

56 Color Image Sharpening We can do in the same manner as color image smoothing: 1. Per-color-plane method for RGB,CMY images 2. Sharpening onl I component of a HSI image Sharpening all RGB components Sharpening onl I component of HSI

57 Color Image Sharpening Eample cont. Difference between sharpened results from 2 methods in the previous slide.

58 Color Segmentation 2 Methods: 1. Segmented in HSI color space: A thresholding function based on color information in H and S Components. We rarel use I component for color image segmentation. 2. Segmentation in RGB vector space: A thresholding h function based on distance in a color vector space.

59 Color Segmentation in HSI Color Space Cl Color image Hue Saturation Intensit Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

60 Color Segmentation in HSI Color Space cont. Binar thresholding of S component Product of 2 and 5 with T = 10% 5 6 Red piels Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition. 7 8 Histogram of 6 Segmentation of red color piels

61 Color Segmentation in HSI Color Space cont. Cl Color image Segmented results of red piels Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

62 Color Segmentation in RGB Vector Space 1. Each point with R,G,B coordinate in the vector space represents one color. 2. Segmentation is based on distance thresholding in a vector space g, 1 0 Du,v = distance function if if D c,, ct D c,, c T T T c T = color to be segmented. c, = RGB vector at piel,.

63 Eample: Segmentation in RGB Vector Space Color image Reference color c T to be segmented c average color of piel in the bo c T Results of segmentation in RGB vector space with Threshold value T = 1.25 times the SD of R,G,B values In the bo

64 Gradient of a Color Image Since gradient is define onl for a scalar image, there is no concept of gradient for a color image. We can t compute gradient of each color component and combine the results to get the gradient of a color image. Red Green Blue We see 2 objects. We see 4 objects. Edges

65 Gradient of a Color Image cont. Gradient of a Color Image cont. One wa to compute the maimum rate of change of a color image One wa to compute the maimum rate of change of a color image which is close to the meaning of gradient is to use the following formula: Gradient computed in RGB color space: p p 2 1 sin 2 2 cos2 1 g g g g g F sin 2 2 cos2 2 g g g g g F g 2 1 g g 2g tan B G R g B G R g B B G G R R g g

66 Gradient of a Color Image Eample 2 Original image Obtained using the formula in the previous slide 3 Sum of gradients of Difference each color between component 2 and 3 3

67 Gradient of a Color Image Eample Red Green Blue Gradients of each color component

68 Noise in Color Images Noise can corrupt each color component independentl. AWGN 2 =800 AWGN 2 =800 AWGN 2 =800 Ni Noise is less noticeable in a color image Images from Rafael C. Gonzalez and Richard E. Wood, Digital Image Processing, 2 nd Edition.

69 Noise in Color Images Hue Saturation Intensit

70 Noise in Color Images Salt & pepper noise in Green component He Hue Saturation Intensit

71 Color Image Compression Original image JPEG2000 File After loss compression with ratio 230:1