Image Enhancement via Multi-Gaussian Histogram Specification
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Solution Overview
Problem
Existing image enhancement methods, such as histogram specification using a single Gaussian function, often result in loss of image details during contrast adjustment.
Innovation Solution
An image processing method that utilizes a statistical distribution model, generated using a cloud generator, to enhance image contrast without losing details, by calculating a parameter profile, adjusting it, and applying it to perform histogram specification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If histogram specification using a single Gaussian function is used to adjust image contrast, then the contrast enhancement is achieved, but image details are lost
Solution Approach 1:
The patent segments the single Gaussian function into multiple Gaussian functions with different standard deviations. Each Gaussian function targets different regions of the histogram, allowing selective enhancement of specific intensity ranges while preserving details in other ranges. This segmentation resolves the contradiction by enabling contrast enhancement in specific regions without causing information loss in other regions.
Solution Approach 2:
The patent applies different Gaussian functions with different parameters to different regions of the histogram, creating local quality variations in the enhancement process. By matching Gaussian functions to specific histogram regions based on image characteristics, the method achieves local contrast enhancement while preserving global image details, thus resolving the contradiction between contrast improvement and detail preservation.
2Illumination intensity
If histogram equalization is used to make gray levels uniformly distributed, then contrast is improved, but natural appearance of the image is lost
Solution Approach 1:
The patent changes the parameter of the target distribution from uniform (histogram equalization) to Gaussian with adjustable mean and standard deviation. By dynamically adjusting these parameters based on image characteristics, the method achieves contrast enhancement while maintaining the natural appearance of the image, resolving the contradiction between contrast improvement and natural appearance preservation.
Data Source
AI summary
A method for image processing, which comprises the following steps: Generating a first histogram from a first image; Calculating a first parameter profile from the first image indicative of the quality of the first image; Adjusting the first parameter profile to generate a second parameter profile; Using the second parameter profile to generate a statistical distribution via a statistical distribution generator, wherein the statistical distribution is characterized by at least three parameters; Using the statistical distribution to perform a histogram specification to the first histogram of the first image to generate a second histogram; Generating a second image based on the first image and the second histogram.