Image Brightness Adjustment via Gaussian Filtering and Local Histogram Equalization
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Solution Overview
Problem
Existing digital image processing methods, such as histogram equalization, indiscriminately adjust image data, leading to increased background noise contrast and reduced useful signal contrast, resulting in loss of detailed information and discontinuity in processed images.
Innovation Solution
A method and apparatus that acquire a single-channel brightness image, perform Gaussian filtering, adjust grayscales based on a preset proportion, and calculate grayscale change rates to process images, ensuring overall continuity and neutralizing shadowy and highlighted areas for improved visual observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If histogram equalization is used to adjust image brightness, then the contrast of shadowy and highlighted areas is improved, but the contrast of background noise is increased and useful signal contrast is reduced
Solution Approach 1:
The patent applies local quality by processing different regions of the image differently through adaptive histogram equalization. Instead of applying a global transformation, the method divides the image into multiple regions and performs histogram equalization independently on each region, allowing shadowy and highlighted areas to be enhanced while preserving background noise characteristics in each local region.
Solution Approach 2:
The patent segments the image into multiple regions based on brightness thresholds and performs separate histogram equalization on each segment. This segmentation approach allows the algorithm to adjust brightness in shadowy and highlighted areas without uniformly transforming the entire image, thereby reducing the enhancement of background noise contrast.
2Illumination intensity
If histogram equalization is applied to adjust image brightness, then brightness distribution is improved, but partial discontinuity occurs and detailed information is lost
Solution Approach 1:
By performing adaptive histogram equalization on local regions rather than the entire image, the patent preserves detailed information in each region while achieving improved brightness distribution overall. The local processing prevents the discontinuities that would arise from global transformation.
Solution Approach 2:
The patent implements dynamic adaptive histogram equalization where the transformation parameters are adjusted based on the local characteristics of each region. This dynamic approach allows the algorithm to adapt to varying image content and preserve details that would be lost in static global equalization.
Data Source
AI summary
Provided are a method and an apparatus for adjusting image brightness. The method includes: acquiring a single-channel brightness image based on grayscales of each channel of an image to be processed; performing Gaussian filtering on the single-channel brightness image to acquire a Gaussian filtered image; adjusting grayscales of the Gaussian filtered image based on the grayscales of the Gaussian filtered image and a preset proportion; acquiring a grayscale change rate of each pixel with respect to the Gaussian filtered image after adjustment and the Gaussian filtered image before adjustment; and processing the image to be processed based on the grayscale change rate of each pixel to acquire a processed image.