Auto-Exposure Correction via Histogram Transfer Curves
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Solution Overview
Problem
Existing image acquisition systems face limitations in improving the quality of raw image information through histogram adjustment, as it does not effectively address exposure and white balance issues, particularly in varying light conditions.
Innovation Solution
The method involves calculating a correction factor based on a cumulative image histogram to adjust exposure parameters and sensor gains for each color plane, allowing for improved exposure and white balance correction by mapping the histogram to a target distribution, which can be applied between image acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If histogram adjustment is applied to acquired images, then image contrast is improved, but the quality of raw image information is not improved
Solution Approach 1:
The patent applies histogram adjustment to the cumulative histogram before image acquisition to pre-calculate correction factors for exposure parameters and sensor gains. This preliminary action ensures that the raw image information is captured with correct exposure and white balance, rather than correcting after acquisition. The transfer curve is determined in advance based on target cumulative histogram, and these pre-computed corrections are applied during image capture to improve raw image quality directly.
2Illumination intensity
If exposure parameters are adjusted based on overall image histogram, then average brightness is improved, but color accuracy and contrast are not optimized
Solution Approach 1:
The patent segments the cumulative histogram analysis by color plane (R, G, B channels) and intensity range (focusing on brightest pixels). Instead of applying a single overall histogram correction, separate transfer curves are determined for each color plane's cumulative histogram. This segmentation allows independent optimization of exposure and white balance for each color channel, improving both average brightness and color accuracy simultaneously.
Solution Approach 2:
The patent applies local quality by focusing the histogram analysis and transfer curve determination specifically on the brightest portions of the cumulative histogram (top percentile of pixels). This local approach prioritizes accurate representation of highlights and bright areas, which contain critical information about light source color and scene illumination. The correction factors derived from this local analysis are then applied globally to improve overall image quality while maintaining color accuracy.
3Measurement precision
If sensor gains are adjusted for white balance, then color balance is improved, but exposure level may become suboptimal
Solution Approach 1:
The patent merges the exposure correction and white balance correction into a unified process by determining transfer curves for each color plane's cumulative histogram simultaneously. The exposure correction factor (from overall cumulative histogram) and white balance correction factors (from individual color plane cumulative histograms) are calculated together based on the same target cumulative histogram. This merging ensures that both exposure level and white balance are optimized in a coordinated manner, avoiding the trade-off between the two corrections.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the acquisition of images with enhanced exposure and white balance, resulting in improved contrast and color accuracy, particularly by focusing on the brightest pixels to accurately represent the light source's color, thus enhancing the overall image quality.
Implementation Method 1
The image sensor converts the incoming light energy into electrical signals that are processed to produce an image of the scene
Data Source
AI summary
An auto-exposure module determines a first set of exposure parameters to acquire a first image. The first image is acquired using the first set of exposure parameters. The module determines a target image histogram; calculates an image histogram based on intensity values of the pixels of the first image; determines an integral of the image histogram and of the target image histogram for a range of pixel intensity values; calculates a transfer curve for transforming the integral of the image histogram to match the integral of the target image histogram; calculates a slope of a line fitting at least a portion of the transfer curve; determines a correction factor based on the calculated slope; and adjusts the first set of exposure parameters according to the correction factor. A second image is then acquired using the adjusted first set of exposure parameters.