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Transferring Color to Greyscale Images

Learn about the process of translating color into black-and-white images, the challenges faced in colorization, and how advanced techniques can enhance various types of imagery. Explore the innovative methods developed by experts at Stony Brook University, showcased at SIGGRAPH 2002, that revolutionize the way we perceive and manipulate digital visual content. From converting old movies to adding depth to scientific scans, colorization opens up a world of possibilities with practical applications in multiple industries. Dive into the technical intricacies behind creating a color space like LAB and discover the intricacies of luminance mapping for optimal results. Witness how this technology has transformed the entertainment and medical fields, while also considering the ethical and financial implications of its widespread adoption. Stay informed on the latest advancements in colorization, and unlock the potential for creative expression and data interpretation using MATLAB.

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Transferring Color to Greyscale Images

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  1. Transferring Color to Greyscale Images Tomihisa Welsh, Michael Ashikhmin, Klaus Mueller (Stony Brook University) SIGGRAPH2002

  2. What is Colorization? • Woody Allen: • "colorization" of films "is a `monstrous,' disgusting,' horrible,' `sinful,' `absurd,' `humiliating,' `preposterous,' and `insulting' mutilation and defacing of genuine works of art, in which computers are used to `doctor' and `tamper' with the `great originals,' thereby creating `degraded,' `cheesy,' artificial symbols of one society's greed."

  3. What can it do? • “Colorize” old movies • Make black and white pictures color • Adds a dimension to MRI’s or airport luggage scans • Lifelike electron microscope scans

  4. Why is it difficult? • Grayscale images consist of one dimensional data • Luminance data • No Saturation or Hue • 3D HSL colors have 256x256 or ~66,000 colors with a given Luminance

  5. It’s still not easy… • Different sections of an image might have same lumination but very different hue.

  6. But let’s give it a shot • We need a color space • HSL and RGB just wont cut it • Domo Arigato Mr. Ruderman et. Al. 1998 • Created l, a, B color space • Decorellated space • Linearly independent • Luminance ( l ) • Yellow-blue ( a ) • Red-Green ( B )

  7. Come again? • Conversion: • LMS = Long, medium, and short wavelength • Now that we have our new color space, we turn to some sadis…I mean statistics

  8. Scale and correct l, a, and B

  9. RGB to LAB

  10. Luminance Mapping • Source image = color image • Target image = grayscale image

  11. Luminance Mapping part Deux • Hertzmann et. Al. 2001 • Standard deviation of 5x5 pixel neighborhood • Match source and target based on combination of luminance (50%) and std. dev. (50%) • Map the match

  12. Map it Source Luminance Channel Target Remapped

  13. Houston… • There is a problem with this. • What if there are different sections of the picture?

  14. Swatches • Indicate similar parts of a scene

  15. Examples

  16. More Examples

  17. Video • Why not extend this to video? • Industry born in 1970 (Wilson Markle) • Recolorized movies use these techniques • Source frame used every camera change • Once have first target, use that as source • User-defined swatches can track movement

  18. Waves

  19. Horses

  20. This is your brain

  21. At what cost? • In 1987 $3,000/min or $300,000/movie • One Critic calls it the “Bastardization” of film • Brings in on average $500,000/release

  22. Oh, Cool

  23. Wrap-Up • Get source and target • Convert colors to l, a, B • Map colors • Use swatches for faster better results • Process takes between 15 seconds and 4 minutes on Pentium III 900 MHz CPU • Use MATLAB

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