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Image Enhancement via Adaptive Unsharp Masking

Image Enhancement via Adaptive Unsharp Masking. By Isha Jha. 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

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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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