Logarithmic Image Processing (LIP)

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Logarithmic Image Processing (LIP) - PowerPoint PPT Presentation

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

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Presentation Transcript

Logarithmic Image Processing (LIP)

By

Ben Weisenbeck

Oiki Wong

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.

Key Things

α governs the contrast of the image

β governs the sharpness of the image

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
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.

Color Enrichment

Histogram Equalization creates an illusion that the flight was in bad weather!

Window Sizing

LIP with 3x3 window

LIP with 9x9 window

Noise

F(ij)=B(i,j)+noise

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
Conclusion