Grayscale Adjustment via Adaptive Histogram Mapping
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Solution Overview
Problem
Existing image contrast enhancement techniques, such as histogram equalization, often result in issues like voids in the output histogram, excessive enhancement, lack of control parameters, and uncontrollable noise, failing to effectively adjust grayscale values and maintain image details.
Innovation Solution
A method and device that calculate adjusted grayscale values for images using a histogram equalization mapping function, with additional mapping values and weights to manage pixel values within specific ranges, ensuring the dynamic range is enhanced without excessive noise or detail loss, by selecting minimum or maximum values when preset values exceed the grayscale range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If histogram equalization is used to enhance image contrast, then the dynamic range of grayscale values is increased, but voids appear in the output histogram and excessive enhancement occurs
Solution Approach 1:
The patent modifies the histogram equalization mapping function by introducing a parameter λ (lambda) that controls the shape of the mapping curve. By adjusting this parameter, the system can transform the linear equalization mapping into a nonlinear mapping that avoids voids in the histogram while maintaining contrast enhancement. The mapping function becomes: s = (L-1) * (1 - (1 - c(r)/L)^(1/λ)), where λ > 0 controls the curvature and distribution characteristics.
Solution Approach 2:
The patent introduces adaptability by allowing the mapping function parameters to be adjusted based on the input image characteristics. The system dynamically selects or adjusts the parameter λ according to the specific image's histogram distribution, making the contrast enhancement process adaptive rather than static. This dynamic adjustment prevents excessive enhancement and avoids creating voids in the output histogram.
2Illumination intensity
If histogram equalization is applied to pull apart grayscale values, then contrast is enhanced, but noise and banded interference become uncontrollable
Solution Approach 1:
By modifying the mapping function parameter λ, the patent creates a smoother transformation that reduces abrupt changes in grayscale values. This parameter adjustment softens the enhancement effect in regions where it would otherwise create noise or banding artifacts, while still maintaining overall contrast improvement.
3Illumination intensity
If traditional histogram equalization is used, then grayscale distribution is averaged, but control parameters are not provided and excessive enhancement occurs
Solution Approach 1:
The patent introduces controllable parameters (λ and potentially others) that allow users to adjust the enhancement strength and characteristics. This provides direct control over the output, enabling operators to prevent excessive enhancement while maintaining the grayscale distribution improvements.
Data Source
AI summary
A method for adjusting grayscale values of an image includes: for each pixel in the image, calculating a first mapping value for a grayscale value of the pixel in the image through a histogram equalization mapping function; calculating a second mapping value by subtracting a first preset value from the grayscale value of the pixel through the histogram equalization mapping function, and when the result exceeds a preset grayscale range, selecting a minimum value of the preset grayscale range for calculating the second mapping value; calculating a third mapping value by adding a second preset value to the grayscale value of the pixel through the histogram equalization mapping function and when the result exceeds the preset grayscale range, selecting a maximum value of the preset grayscale range; and calculating an output grayscale value of the pixel based on the first, second, and third mapping value and respective weights.