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A nonlinear image contrast sharpening approach based on Munsell's scale
Sean C Matz1, Rui J P de Figueiredo
1Boeing Company, Seal Beach, CA 90740, USA.
Summary
This study presents a novel nonlinear local contrast enhancement method using the Munsell value scale. The technique preserves original shades of gray, avoiding image distortion for improved visual perception.
Area of Science:
- Image Processing
- Computer Vision
- Human Visual Perception
Background:
- Contrast quantifies intensity variation in image regions, globally or locally.
- Existing methods may alter image gray values during contrast enhancement.
Purpose of the Study:
- Introduce a nonlinear local contrast enhancement method.
- Preserve original image gray values and subinterval groupings.
- Enhance image contrast based on human visual perception.
Main Methods:
- Utilized the Munsell value scale for gray scale partitioning into ten subintervals.
- Developed a contrast enhancement function approximating a threshold step function within each subinterval.
- Applied local processing using a smooth thresholding approach based on mean edge gray value.
Main Results:
- Successfully enhanced local contrast while preserving original shades of gray.
- Maintained the integrity of gray value groupings by subinterval.
- Demonstrated a method that avoids gray value distortion in enhanced images.
Conclusions:
- The proposed nonlinear local contrast enhancement method effectively improves image contrast.
- The method's reliance on the Munsell value scale ensures preservation of original image characteristics.
- This approach offers a robust solution for contrast enhancement without introducing artifacts.