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Transferring Color to Greyscale Images

This project explores techniques for colorizing greyscale images, enhancing scientific data like Medical Imaging (MRI, CT, X-Ray), Satellite Images (Landsat), and Scanning Electron Microscopy (SEM). It aims to tackle the challenge of extrapolating color information from 1-D to 3-D, mapping luminance to RGB vectors. Existing methods like manual coloring and global color mapping are analyzed, and a novel approach focusing on pixel neighborhood statistics is introduced. The process involves converting images to the lαβ color space, matching pixel colors, and transferring color efficiently. With applications in black-and-white photographs, movies, and artistic effects, this method offers a creative solution with reduced manual effort.

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Transferring Color to Greyscale Images

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  1. Transferring Color to Greyscale Images Tomihisa (Tom) Welsh Michael Ashikhmin Klaus Mueller Center for Visual Computing Stony Brook University

  2. The Problem • How to colorize greyscale images?

  3. The Problem • How to colorize greyscale images?

  4. The Problem • How to colorize greyscale images? • Is his tie blue or green?

  5. The Problem • How to colorize greyscale images? • Red!!

  6. The Problem • How to colorize greyscale images? • Issues: • No “correct” solution • Need to be creative • How to minimize the manual labor involved?

  7. Motivation • Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray)

  8. Motivation • Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray) • Satellite Images (Landsat)

  9. Motivation • Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray) • Satellite Images (Landsat) • Scanning Electron Microscopy (SEM)

  10. Motivation • Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray) • Satellite Images (Landsat) • Scanning Electron Microscopy (SEM) • Colorize black/white photographs and movies

  11. Motivation • Enhance Scientific Data • Medical Imaging (MRI, CT, X-Ray) • Satellite Images (Landsat) • Scanning Electron Microscopy (SEM) • Colorize black/white photographs and movies • Artistic Effects

  12. The Task • The problem is fundamentally ill-posed • It is an attempt to extrapolate from 1-D to 3-D • Map scalar luminance (intensity) to vector RGB

  13. Previous Methods • Coloring Book Method • Photoshop: manually paint color with low opacity

  14. Previous Methods • Coloring Book Method • Photoshop: manually paint color with low opacity • Movie Industry: track polygons [Cinesite Press Article]

  15. Previous Methods • Coloring Book Method • Photoshop: manually paint color with low opacity • Movie Industry: track polygons [Cinesite Press Article] • Pseudo-coloring • Global Transformation/Color Map

  16. Previous Methods • Coloring Book Method • Photoshop: manually paint color with low opacity • Movie Industry: track polygons [Cinesite Press Article] • Pseudo-coloring • Global Transformation/Color Map • Satellite Images • Registration [R2V Software], Orthoimagery [Premoze]

  17. Previous Methods • Coloring Book Method • Photoshop: manually paint color with low opacity • Movie Industry: track polygons [Cinesite Press Article] • Pseudo-coloring • Global Transformation/Color Map • Satellite Images • Registration [R2V Software], Orthoimagery [Premoze] • Image Analogies • Grey Source : Color Source :: Grey Target : Result

  18. Related Work • “Color Transfer between Images” [Reinhard et al. 2001]

  19. Related Work • “Color Transfer between Images” [Reinhard et al. 2001] + Source Target

  20. Related Work • “Color Transfer between Images” [Reinhard et al. 2001] + = Source Final Target

  21. Reinhard et al. • Decorrelated color space (lαβ) [Ruderman et al., 1998] • Scale and shift color distributions globally (Using mean and standard deviation) Target Source

  22. Reinhard et al. • Decorrelated color space (lαβ) [Ruderman et al., 1998] • Scale and shift color distributions globally (Using mean and standard deviation) Target (Scaled) Source

  23. Reinhard et al. • Decorrelated color space (lαβ) [Ruderman et al., 1998] • Scale and shift color distributions globally (Using mean and standard deviation) Target (Scaled & Shifted) Source

  24. Our Approach Target

  25. ??? Our Approach Target • Select color source image Source + ???

  26. Our Approach Target • Select color source image • Match each target pixel with a few sourcepixels Source +

  27. Our Approach Target • Select color source image • Match each target pixel with a few sourcepixels • Choose best match using local pixel neighborhood statistics Source +

  28. Our Approach Target • Select color source image • Match each target pixel with a few sourcepixels • Choose best match using local pixel neighborhood statistics • Transfer color Source + =

  29. Our Approach Target Final • Select color source image • Match each target pixel with a few sourcepixels • Choose best match using local pixel neighborhood statistics • Transfer color • Repeat for all pixels Source + =

  30. Global Image Matching Procedure • Convert images to lαβ color space • Image Matching • Color Transfer

  31. 1. Convert to lαβ Space • Luminance (l), alpha (α) and beta (β) channels • Minimizes correlation between axes (i.e. cross-channel artifacts) Color Image RGB lαβ

  32. 2. Image Matching • Remap luminance histograms between src/target Source-Before Target Source-After

  33. 2. Image Matching • Remap luminance histograms between src/target • Precompute neighborhood statistics for images

  34. 2. Image Matching • Remap luminance histograms between src/target • Precompute neighborhood statistics for images • Reduce samples using jittered sampling - Faster computation due to smaller search space

  35. 2. Image Matching • Remap luminance histograms between src/target • Precompute neighborhood statistics for images • Reduce samples using jittered sampling - Faster computation due to smaller search space

  36. 2. Image Matching • Remap luminance histograms between src/target • Precompute neighborhood statistics for images • Reduce samples using jittered sampling - Faster computation due to smaller search space

  37. 2. Image Matching • Remap luminance histograms between src/target • Precompute neighborhood statistics for images • Reduce samples using jittered sampling - Faster computation due to smaller search space • Find best neighborhood match from samples - Weighted metric of luminance, mean, standard deviation

  38. 3. Color Transfer • Transfer only alpha and beta channels (color) Target Image Colorized Results

  39. 3. Color Transfer • Transfer only alpha and beta channels (color) • The original luminance value remains unchanged Target Image Colorized Results Convert to Greyscale (Photoshop)

  40. Results: satellite + Source Target

  41. Results: satellite + = Source Target Final

  42. Results: Textures + Source Target

  43. Results: Textures + = Source Target Final

  44. Limitations (Global Approach) + = Source Target Final

  45. Limitations (Global Approach) + = + = Source Target Final

  46. Limitations (Global Approach) + = + = Source Target Final

  47. User-Assisted Approach • User selects small number of swatches • Transfer color only to swatches (Global Matching Procedure) • Color entire target image (Only use swatch samples)

  48. 1. Selection of Swatches Source Target

  49. 2. Transfer Color to Swatches • Transfer color using Global Matching Procedure (described previously)

  50. 3. Colorize Entire Image • Discard original source image

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