Adaptive Clipped Histogram Equalization for Image Contrast Enhancement
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Solution Overview
Problem
Conventional image processing methods like standard histogram equalization (SHE) and its variants, such as bi-histogram equalization (BHE) and clipped histogram equalization (CHE), fail to effectively enhance image contrast while maintaining mean brightness and are not adaptive to image brightness variations, leading to increased noise and suboptimal contrast enhancement.
Innovation Solution
An image processing method that adaptively controls the histogram clipping rate and distributes gain differently across image regions, using a computed clipping threshold and local/global gains to correct the brightness histogram, thereby enhancing image contrast without destroying image information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If standard histogram equalization (SHE) is used to enhance image contrast, then image contrast is improved, but mean brightness changes substantially and noise increases
Solution Approach 1:
The patent applies different processing strategies to different regions of the histogram based on local characteristics. The adaptive clipping threshold dynamically determines which histogram regions to clip, allowing contrast enhancement in dark regions while preserving brightness in other regions, thus resolving the contradiction between contrast improvement and brightness preservation
Solution Approach 2:
The patent changes the clipping threshold parameter adaptively based on image characteristics rather than using a fixed threshold. This dynamic parameter adjustment allows the algorithm to optimize both contrast enhancement and brightness preservation for different images and regions, resolving the contradiction by making the processing parameters adaptive rather than static
2Device complexity
If conventional clipped histogram equalization (CHE) with fixed threshold is used, then computation is simplified, but adaptability to image brightness variations is reduced
Solution Approach 1:
The patent transforms the static fixed threshold of conventional CHE into a dynamic adaptive threshold that changes based on image brightness characteristics. This dynamic adjustment maintains computational simplicity while significantly improving adaptability to different image conditions, resolving the contradiction between simplicity and versatility
3Loss of information
If histogram segmentation methods (BHE, RMSHE) are used to maintain brightness, then mean brightness is preserved, but device complexity and computation increase
Solution Approach 1:
The patent extracts and clips only the specific upper portion of the histogram that causes brightness shifts, rather than performing complex multi-histogram segmentation. This selective clipping approach preserves brightness while avoiding the computational complexity of generating and processing multiple separate histograms, resolving the contradiction between brightness preservation and computational simplicity
4Manufacturing precision
If clipped histogram equalization is applied to enhance contrast, then image contrast is improved, but noise increases in certain regions like black level region
Solution Approach 1:
The patent applies local quality by using adaptive clipping thresholds that respond to local image characteristics. This allows contrast enhancement to be applied selectively where needed while avoiding excessive enhancement in noise-prone regions, thus improving contrast without uniformly increasing noise across all regions
Data Source
AI summary
An image processing method and system using gain-controllable clipped histogram equalization, in which the image processing method includes the steps of as obtaining a brightness histogram, computing a mean brightness of an image signal, determining a clipping rate based on the mean brightness, determining a clipping threshold based on the clipping rate, obtaining a clipped brightness histogram by clipping frequencies exceeding the clipping threshold in the brightness histogram, obtaining a corrected brightness histogram by correcting the clipped brightness as histogram using the clipping rate as a total gain, obtaining a cumulative histogram from the corrected brightness histogram, and correcting an input image using the cumulative histogram as a transformation function. Accordingly, the clipping rate is adaptively controlled so that image contrast is enhanced.