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Image Enhancement via Adaptive Unsharp Masking

Learn about the technique of Unsharp Masking to highlight high-frequency contents in images. Discover how to control contrast levels and navigate the disadvantages and solutions using Adaptive Unsharp Masking. Explore algorithms, results, and measures of enhancement quality with an adaptive approach.

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Image Enhancement via Adaptive Unsharp Masking

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  1. Image Enhancement via Adaptive Unsharp Masking By Isha Jha

  2. What is Unsharp Masking? • Want to emphasize high frequency contents of an image. • Z(n,m) is a high pass filtered version of image • l controls the level of contrast enhancement • Disadvantages: Noise in smooth regions Overshoot artifacts

  3. Objective • Increasing dynamics in smooth areas amplifies noise – so no emphasis • High contrast areas already have high local dynamics – Require low enhancement to avoid overshoot • Medium contrast areas require most enhancement

  4. Solution • Use Adaptive Unsharp Masking- l varies according to predefined rules Z

  5. Algorithm • Measure of Local Dynamics g(.)

  6. Algorithm cont… • b – positive convergence parameter • m – step size

  7. Schematic

  8. Results

  9. Results cont…

  10. Measuring Quality of Enhancement • Desired Behavior given by • e(n,m)=gd(n,m)-gy(n,m) • Look at e2 • Cost Function

  11. Conclusion • The adaptive approach prevents highlighting of noise in smooth areas • In areas with high contrast it produces medium enhancement to avoid artifacts • Highest enhancement in regions with medium contrast

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