Image Contrast Enhancement via Adaptive Segmentation
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Solution Overview
Problem
Traditional image processing methods, such as histogram equalization, often result in unsatisfactory contrast enhancement, leading to excessive contrast in some areas and loss of detail in others, failing to meet quality requirements in special scenarios.
Innovation Solution
A method that determines segmentation points and corresponding brightness conversion functions based on pixel statistical data to divide and adjust brightness values, preventing excessive contrast and preserving details in small areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If traditional histogram equalization method is used to enhance image contrast, then overall contrast is improved, but contrast becomes too high or is sacrificed in small areas, leading to loss of information in certain brightness grey levels
Solution Approach 1:
The patent divides the pixel statistical data into multiple partial statistical data sets using segmentation points. Each partial data set corresponds to different brightness ranges and is processed independently with its own conversion function, preventing uniform contrast enhancement from causing information loss in specific areas.
Solution Approach 2:
The patent applies different brightness conversion functions to different partial statistical data sets based on their specific characteristics. This localized processing ensures that each region's brightness details are preserved while still achieving contrast enhancement, avoiding the one-size-fits-all approach of traditional methods.
2Productivity
If traditional image processing algorithms are applied uniformly to the entire image, then processing is simple and fast, but the results are not satisfactory in special scenarios with varying brightness distributions
Solution Approach 1:
The patent dynamically determines segmentation points based on the actual pixel statistical data of each image. This adaptive approach allows the processing method to adjust to different brightness distributions in special scenarios while maintaining efficient processing through automated threshold calculation.
Solution Approach 2:
The patent changes the processing parameters (segmentation points and conversion functions) based on the specific characteristics of each image's pixel statistical data. This parameter adaptation enables high-quality processing results across various scenarios without sacrificing processing efficiency.
Data Source
AI summary
The present invention describes enhancing image contrast comprising the following steps: determining a segmentation point according to at least one segmentation threshold and pixel statistical data of a picture, wherein the segmentation point is utilized to divide the pixel statistical data into a first partial statistical data and a second partial statistical data; and determining a first partial brightness conversion function and a second partial brightness conversion function according to the first and the second partial statistical data, respectively. The first partial brightness conversion function is used to adjust brightness values of multiple pixels in the picture corresponding to the first partial statistical data, and the second partial brightness conversion function is used to adjust brightness values of multiple pixels in the picture corresponding to the second partial statistical data.