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Logarithmic Image Processing (LIP)

Logarithmic Image Processing (LIP) offers a unique approach to simultaneous contrast stretching and image sharpening. In this methodology, parameters α and β control the contrast and sharpness of the image, respectively. By tuning these parameters carefully, users can enhance specific features rather than relying solely on histogram equalization, which may not effectively highlight desired elements. This guide explores the advantages of LIP over traditional methods, detailing its application on both color and black-and-white images, and its effectiveness in reducing noise and enriching colors.

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Logarithmic Image Processing (LIP)

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  1. Logarithmic Image Processing (LIP) By Ben Weisenbeck Oiki Wong

  2. Introduction Q: Why LIP? A: Contrast Stretching and Image Sharpening simultaneously Q: Why not histogram equalization? A: A flat histogram often times are not what is needed to enhance certain features within the image.

  3. Key Things α governs the contrast of the image β governs the sharpness of the image

  4. Main Idea Tune the parameters until you get what you want. But first, know what each parameter does! • α governs the contrast of the image: • α >1 Brings out bright areas • α <1 Brings out dark areas • α < 0 Negative Transformation • β governs the sharpness of the image: • β >1 Sharpening • β <1 Blurring • n x n window also governs the sharpness of the image • Bigger is not always better

  5. Examples of Histogram Equalization isn’t the answer

  6. Examples of Histogram Equalization isn’t the answer

  7. Graphical User Interface

  8. Graphical User Interface

  9. Enhancing Dark Details

  10. Enhancing Dark Details

  11. Enhancing Dark Details

  12. Enhancing Bright Details

  13. Color Enrichment Our result shows that the algorithm can also create color enrichment to a certain degree. This is something that histogram equalization fails to perform. The color in the words “U.S AIR FORCE” stands out much more in the enhanced image. Also note that the shadow in the mountain is “deeper” than the original.

  14. Color Enrichment Histogram Equalization creates an illusion that the flight was in bad weather!

  15. Window Sizing LIP with 3x3 window LIP with 9x9 window

  16. Noise F(ij)=B(i,j)+noise

  17. Summary • Parameters • α controls the contrast enhancement • β controls the sharpness of the image. • Larger Window size => sharper edges • Superior to Histogram Equalization • Black and White images • Color images • Noisy Images

  18. Conclusion • Advantages • Simultaneous enhancement of contrast and sharpness • Fine-tuned control over image enhancement • Disadvantage • Parameter values must be carefully selected and adjusted to obtain desirable results

  19. Questions?

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