10 color image processing

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10 color image processing

  1. 1. Color Image Processing : 1 Color Image ProcessingColor Image Processing
  2. 2. Color Image Processing : 2 Visible LightVisible Light Visible light composed of relatively narrow band of frequencies in electromagnetic spectrum Chromatic light spans EM spectrum from around 400 to 700 nm
  3. 3. Color Image Processing : 3 Color PerceptionColor Perception Perceived color of an object based on nature of light reflected from object Examples: If object reflects light that's balanced from all visible wavelengths, object is perceived as white If object reflects light with wavelengths mainly in the 575 to 625nm range, object is perceived as red
  4. 4. Color Image Processing : 4 Cones RevisitedCones Revisited 6 to 7 million cones in the human eye Divided into three main types: L cones (65%)  Maximally sensitive to long wavelengths (e.g., red) M cones (33%)  Maximally sensitive to medium wavelengths (e.g., green) S cones (2%)  Maximally sensitive to short wavelengths (e.g., blue)
  5. 5. Color Image Processing : 5 Light Absorption of ConesLight Absorption of Cones Visible colors can be visualized as weighted combination of primary colors red, green, and blue
  6. 6. Color Image Processing : 6 Mixtures of Light vs. Mixtures of PigmentsMixtures of Light vs. Mixtures of Pigments Mixture of light primaries additive Mixture of pigment primaries subtractive
  7. 7. Color Image Processing : 7 CIE Chromaticity DiagramCIE Chromaticity Diagram A method for specifying colors Specifies color composition as function of x (red) and y (green) For any value of x and y, value of z (blue) can be found as The (x,y,z) values of a color specifies percentage of red, green, and blue needed to form the color (Trichromatic Coefficients) 1 ( )z x y= − +
  8. 8. Color Image Processing : 8 CIE Chromaticity DiagramCIE Chromaticity Diagram
  9. 9. Color Image Processing : 9 CIE Chromaticity DiagramCIE Chromaticity Diagram
  10. 10. Color Image Processing : 10 CIE Chromaticity DiagramCIE Chromaticity Diagram InterpretationInterpretation Pure spectrum colors located around boundary All non-boundary colors are mixture of spectrum colors Point of equal energy corresponds to equal fractions of the three primary colors CIE standard for white light Straight line segment joining two points define all colors that can be created by mixing these two colors additively
  11. 11. Color Image Processing : 11 RGB Color ModelRGB Color Model Primarily used for displays and cameras Based on Cartesian coordinate system Three axis represents intensities of red, green, and blue Gray scale (points of equal RGB values) extends from black (0,0,0) to white (1,1,1) Example: 24-bit color (Truecolor) 8-bits (256 levels) are used to represent each channel Gives a total of (256)3 =16,777,216 possible colors!
  12. 12. Color Image Processing : 12 RGB Color Model VisualizationRGB Color Model Visualization
  13. 13. Color Image Processing : 13 CMY/CMYK Color ModelsCMY/CMYK Color Models Primarily used for printing Based on primary colors of pigments For CMY, the three axis represent the amount of cyan, magenta, and yellow pigments to put in to produce a certain color 1 1 1 C R M G Y B            = −                
  14. 14. Color Image Processing : 14 Why K?Why K? In theory, equal amounts of cyan, magenta, and yellow produces black In practice, combining them results in muddy-looking black To produce true black in printing, a fourth color (black) is added to produce the CYMK color model
  15. 15. Color Image Processing : 15 Pros and Cons of RGBPros and Cons of RGB Advantages of RGB model: Straightforward (great for hardware implementation) Matches well with human vision system's strong response to red, green, and blue Disadvantage of RGB model: Difficult for human description of color (e.g., humans don't describe color as RGB percentages) Highly redundant and correlated (e.g., all channels hold luminance information, reduces coding efficiency)
  16. 16. Color Image Processing : 16 HSI Color ModelHSI Color Model Useful for human color interpretation Three axis represent: Hue  Describes pure/dominate color perceived by observer (e.g., pure yellow, orange, red) Saturation (Purity of color)  Amount of white light mixed with hue  High saturation = high purity = little white light mixed with hue Intensity  Brightness
  17. 17. Color Image Processing : 17 Relationship between RGB and HSIRelationship between RGB and HSI Hue: all colors on plane defined by white, black, and a pure color corner point have same hue Saturation: distance from associated pure color Intensity: projection to gray scale line
  18. 18. Color Image Processing : 18 Relationship between RGB and HSIRelationship between RGB and HSI
  19. 19. Color Image Processing : 19 HSI Color Model VisualizationHSI Color Model Visualization
  20. 20. Color Image Processing : 20 Converting colors from RGB to HSIConverting colors from RGB to HSI if 360 if B G H B G θ θ ≤ =  − > ( ) ( ) ( ) ( ) ( ) 1 1 2 2 1 2cos R G R B R G R B G B θ −   − + −    =     − + − −    3 1 [min( , , )]S R G B R G B = − + + [ ] 1 3 I R G B= + +
  21. 21. Color Image Processing : 21 Converting colors from HSI to RGBConverting colors from HSI to RGB When H is in RG Sector (1 )B I S= − 0 cos 1 cos(60 ) S H R I H   = + −  3 ( )G I R B= − + 0 0 (0 120 )H≤ ≤
  22. 22. Color Image Processing : 22 Converting colors from HSI to RGBConverting colors from HSI to RGB When H is in GB Sector (1 )R I S= − 0 cos 1 cos(60 ) S H G I H   = + −  3 ( )B I R G= − + 0 0 (120 240 )H≤ ≤ 0 120H H= −
  23. 23. Color Image Processing : 23 Converting colors from HSI to RGBConverting colors from HSI to RGB When H is in RG Sector (1 )G I S= − 0 cos 1 cos(60 ) S H B I H   = + −  3 ( )R I G B= − + 0 0 (240 360 )H≤ ≤ 0 240H H= −
  24. 24. Color Image Processing : 24 Pseudocolor Image ProcessingPseudocolor Image Processing Goal Assign color to gray levels to convert grayscale image into color image Why? Improve visualization of image information Motivation Humans can discern thousands of color shades but only two dozen or so gray shades
  25. 25. Color Image Processing : 25 Intensity SlicingIntensity Slicing One of the simplest methods for pseudocolor image processing Grayscale image can be viewed as 3D function (x,y, and intensity) Suppose we define P planes perpendicular to intensity axis Each plane i is associated with a color Ci Pixels with intensities lying along a particular plane i is assigned the color Ci corresponding to the plane
  26. 26. Color Image Processing : 26 Visualization of Intensity SlicingVisualization of Intensity Slicing
  27. 27. Color Image Processing : 27 Intensity SlicingIntensity Slicing
  28. 28. Color Image Processing : 28 Intensity Slicing ExampleIntensity Slicing Example
  29. 29. Color Image Processing : 29 Intensity Slicing ExampleIntensity Slicing Example
  30. 30. Color Image Processing : 30 Example: Rainfall MonitoringExample: Rainfall Monitoring
  31. 31. Color Image Processing : 31 Gray Level to Color TransformationsGray Level to Color Transformations Intensity slicing limits range of pseudocolor enhancement results Fixed one-to-one relationship between intensity and specified colors Alternative solution: Process grayscale image using independent transformations The results of the transformations are combined to create one composite color image
  32. 32. Color Image Processing : 32 Example using ThreeExample using Three TransformationsTransformations
  33. 33. Color Image Processing : 33 Example: Security ScreeningExample: Security Screening
  34. 34. Color Image Processing : 34 Transformation 1Transformation 1 Garment bag mapped differently than explosive Easy to spot explosive
  35. 35. Color Image Processing : 35 Transformation 2Transformation 2 Garment bag mapped similar than explosive Hard to spot explosive
  36. 36. Color Image Processing : 36 Multi-Image PseudocoloringMulti-Image Pseudocoloring
  37. 37. Color Image Processing : 37 Example: Multispectral ImageExample: Multispectral Image VisualizationVisualization
  38. 38. Color Image Processing : 38 Point Operations in Color ImagePoint Operations in Color Image ProcessingProcessing Similar to point processing for grayscale images Example: RGB color model n = 3 r1,r2,r3 denotes red, green, blue components of the input image 1 2( , ,..., ), 1,2,...,i i ns T r r r i n= =
  39. 39. Color Image Processing : 39 What are Color Complements?What are Color Complements? Hues opposite one another on the color circle Analogous to grayscale inverses Useful for enhancing details in dark regions of image
  40. 40. Color Image Processing : 40 ExampleExample
  41. 41. Color Image Processing : 41 Point Operations for Tone CorrectionPoint Operations for Tone Correction Tonal range: general distribution of color intensities Similar to intensity contrast in grayscale images High-key images Colors concentrated at high intensities Low-key images Colors concentrated at low intensities As with grayscale images, it is desirable to distribute color intensities evenly
  42. 42. Color Image Processing : 42 Point Operations for Tone CorrectionPoint Operations for Tone Correction Before correcting color imbalances, tonal imbalances are first corrected All color channels are transformed using the same transformation for color models where intensity information is spread across multiple channels (e.g., RGB, CMY) For HSI color model, only I channel is modified Operations are similar to intensity contrast adjustment for grayscale images
  43. 43. Color Image Processing : 43 Tone Correction for Common TonalTone Correction for Common Tonal ImbalancesImbalances Flat images Use an s-curve transformation to boost contrast  lighten highlight areas  darken shadow areas Light and dark images Similar to power-law transformations Stretch light regions and compress dark regions for light images (high gamma) Stretch dark regions and compress light regions for dark images (low gamma)
  44. 44. Color Image Processing : 44 Example Tonal CorrectionsExample Tonal Corrections
  45. 45. Color Image Processing : 45 Point Operations for ColorPoint Operations for Color CorrectionCorrection Various ways to correct color imbalances Perception of a color affected by surrounding colors Proportion of any color (e.g., magenta) can be reduced by Increasing its complementary color (e.g., green) Decreasing portion of the two immediately adjacent colors (e.g., red and blue)
  46. 46. Color Image Processing : 46 Color CorrectionsColor Corrections
  47. 47. Color Image Processing : 47 Histogram EqualizationHistogram Equalization Histogram equalization on individual color channels leads to erroneous colors Better approach is to just equalize intensity component and leave colors (i.e., hues) unchanged
  48. 48. Color Image Processing : 48 Color vision deficienciesColor vision deficiencies Statistics show that color vision deficiencies affect 8.7% of the male population and 0.4% of the female population. Dichromacy is a form of color vision deficiency that severely affects an individual’s ability to differentiate hues. Dichromacy has no known cure.
  49. 49. Color Image Processing : 49 Types of dichromatic color visionTypes of dichromatic color vision deficienciesdeficiencies Protanopia: L cones are absent or defective Deuteranopia: M cones are absent or defective Tritanopia: S cones are absent or defective (rare)
  50. 50. Color Image Processing : 50 Types of dichromatic color visionTypes of dichromatic color vision deficienciesdeficiencies Protanopia and deuteranopia are often referred to as red-green color blindness. Tritanopia is often referred to as blue-yellow color blindness.
  51. 51. Color Image Processing : 51 So what may dichromats see?So what may dichromats see?
  52. 52. Color Image Processing : 52 Color Correction ApproachesColor Correction Approaches There are two main approaches to color correction for helping individuals cope with the medical condition: Fixed color correction Adaptive color correction
  53. 53. Color Image Processing : 53 Fixed Color Correction ApproachFixed Color Correction Approach Perform a fixed color transformation on the image Improves color differentiation to make details more visible Problem: The aesthetics of the original scene is poorly captured
  54. 54. Color Image Processing : 54 Adaptive Color Correction ApproachAdaptive Color Correction Approach Solution: Adapt color transformation based on the underlying hue characteristics of the image Advantages: Improves color differentiation Preserves aesthetic appeal of the original scene
  55. 55. Color Image Processing : 55 Color space transformationColor space transformation The main difficulty encountered by those suffering from dichromacy is the inability to differentiate between certain hues. An effective approach to color enhancement is to alter the hue distribution of an image in such a way that hue discrimination is improved and details within an image become more perceivable by those suffering from dichromacy. To preserve the aesthetic properties of the original image, it is also desired that other characteristics of the image such as illumination and saturation are left unchanged. To accomplish this goal, the image is converted from the RGB color-space to the HSI color-space.
  56. 56. Color Image Processing : 56 Hue RemappingHue Remapping A simple method of improving hue discrimination within the indistinguishable hue range is to perform a circular hue shift such that hues that can be easily discriminated are used to represent this hue range. One of the major disadvantages of this technique is that such a uniform hue shift results in highly unnatural images. The reason for this is that most of the hues that are actually correctly recognized by those suffering from dichromacy are now misrepresented by the hue shift. What if we do a hue compression instead? Allows some of the hues that can be correctly recognized to be assigned much of the same hue as before
  57. 57. Color Image Processing : 57 Hue RemappingHue Remapping Improves hue discrimination and maintains some of the distinguishable hues Problem: the uniform nature of such transforms result in significant loss of dynamic range in the distinguishable portions as well as unnatural color re-mappings in many areas given the fixed redistribution.
  58. 58. Color Image Processing : 58 Non-linear Hue RemappingNon-linear Hue Remapping First, rotate hue space such that the two hues that were indistinguishable are at the front of the spectrum while the third hue is at the end. For example, in the case of protanopia and deuteranopia, the hue range containing the red and green hue components are rotated to the front of the spectrum while the blue hue range is at the end.
  59. 59. Color Image Processing : 59 Non-linear Hue RemappingNon-linear Hue Remapping A hue remapping can then be performed on the rotated hue space in the form of a power transformation function: This hue remapping does two things: The range of hues that are indistinguishable (e.g., red-yellow-green range) are stretched over a wider dynamic range, thereby increasing the hue discrimination for that range of hues. The range of the hue that is distinguishable from the rest of the spectrum (e.g. blue) is compressed, thereby having part of its dynamic range being redistributed to the indistinguishable range. ( )f h hφ =
  60. 60. Color Image Processing : 60 Non-linear Hue RemappingNon-linear Hue Remapping By using a nonlinear remapping function, the range re-distribution is varied over the spectrum and therefore allows for greater flexibility in maintaining the aesthetic feel of the original image. After the hue remapping, the hue space is rotated back to its original position.
  61. 61. Color Image Processing : 61 Adaptive Hue RemappingAdaptive Hue Remapping The parameter controls the curvature of the power function.Φ A simple approach is to set the control parameter at a fixed value. The main problem to this approach is that hue distribution varies greatly from one image to another. For example, an image may consists of only blue hues. Therefore, a fixed value of will compress the blue hue range and stretch theΦ other hue ranges without any perceptual benefit. As such, it is necessary to adaptively adjust the value of basedΦ on the underlying image content to achieve enhanced perceptual quality.
  62. 62. Color Image Processing : 62 Adaptive Hue RemappingAdaptive Hue Remapping If the hue distribution resides mostly in the indistinguishable range, then the control parameter should be increased to stretch this range to improve hue discrimination and attenuate image details. However, if the hue distribution resides mostly outside this range, then the control parameter should be decreased to preserve the original hue distribution. This can be determined based on histogram
  63. 63. Color Image Processing : 63 Examples of Color CorrectionExamples of Color Correction

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