Contrast Adjustment via Sub-Histogram Gamma Remapping
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Solution Overview
Problem
Current histogram equalization methods are unable to perform targeted contrast adjustments on specific areas of images, leading to inefficiencies in image enhancement.
Innovation Solution
A contrast adjustment system and method that accesses an image, generates a histogram, divides it into sub-histograms based on a median value, enlarges and remaps these sub-histograms using a cumulative distribution function to form a gamma curve, and applies it to adjust intensity values in a second image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If histogram equalization is performed on the entire image, then the overall contrast is improved, but the ability to enhance specific areas is lost
Solution Approach 1:
The patent divides the histogram into multiple sub-histograms based on intensity ranges or spatial regions. This segmentation allows different parts of the image to be processed independently, enabling targeted contrast enhancement in specific areas while maintaining overall image quality. The histogram is divided into sub-histograms corresponding to different intensity ranges, allowing selective enhancement.
Solution Approach 2:
The patent applies different contrast enhancement strategies to different regions of the image based on their specific characteristics. By identifying regions of interest and applying localized histogram equalization or gamma correction, the system achieves local quality improvement without compromising the global image appearance. This allows specific areas to receive enhanced processing while other areas maintain their original characteristics.
2Device complexity
If traditional histogram equalization is applied, then the processing is simple, but the contrast adjustment precision is insufficient
Solution Approach 1:
The patent introduces dynamic contrast adjustment by calculating gamma values based on the actual histogram distribution of the image. Instead of using a fixed transformation, the system dynamically computes parameters such as gamma correction factors that adapt to the specific intensity distribution. This dynamic approach enables precise contrast control while maintaining reasonable processing complexity through automated parameter calculation.
Solution Approach 2:
The patent employs parameter transformation techniques, particularly gamma correction with dynamically calculated gamma values. By changing the transformation parameters based on histogram analysis, the system achieves precise contrast adjustment. The gamma value is computed from the histogram data, allowing fine-grained control over the contrast enhancement precision while keeping the overall process manageable.
3Productivity
If histogram equalization is performed without considering intensity distribution, then the process is faster, but the contrast enhancement effectiveness is reduced
Solution Approach 1:
The patent performs preliminary histogram analysis and parameter calculation before applying the contrast enhancement transformation. By pre-computing the histogram distribution and deriving gamma values from this analysis, the system prepares all necessary transformation parameters in advance. This preliminary action allows the actual contrast enhancement to be applied efficiently without requiring complex real-time calculations, thus maintaining both speed and effectiveness.
Data Source
AI summary
A contrast adjustment system includes a memory and a processor. The memory stores instructions. The processor is configured to access and execute the instructions to: access an image with a plurality of pixels, wherein the pixels are corresponding to a plurality of intensity values; generate a histogram based on a distribution of the intensity values in a range from an intensity lower bound to an intensity upper bound; divide the histogram into four sub-histograms based on a median value of the intensity values; enlarge the four sub-histograms based on a predetermined parameter; remap the four sub-histograms to form a gamma curve based on a cumulative distribution function; and apply the gamma curve to pixels of another image.