Imaging Device Contrast Correction Using Adaptive Histogram Scanning
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Solution Overview
Problem
Conventional imaging devices require long processing times to determine upper and lower limit values for grayscale histogram expansion, leading to inaccurate contrast correction due to fixed scanning speeds and threshold values that do not adapt to the shape of the histogram.
Innovation Solution
An imaging device with a contrast correction unit that identifies the shape of the grayscale histogram and adjusts scanning speeds and threshold values based on the histogram's shape, using a shape identifier to optimize the determination of upper and lower limit values by controlling the moving distance between adjacent areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed scanning speeds and threshold values are used for determining upper and lower limit values in grayscale histogram expansion, then the processing logic remains simple, but the processing time becomes long and the accuracy of contrast correction deteriorates
Solution Approach 1:
The patent applies dynamics by making the scanning speed variable rather than fixed. The control unit adjusts the scanning speed based on the histogram shape characteristics, allowing faster scanning when the histogram is flat and slower scanning when the histogram has steep slopes, thereby optimizing both processing time and accuracy adaptively
Solution Approach 2:
The patent changes the scanning speed parameter dynamically based on the histogram shape. By modifying the scanning speed parameter according to the calculated slope and flatness of the histogram, the system achieves accurate limit value determination while reducing processing time
2Adaptability or versatility
If fixed scanning speeds are used for all histogram shapes, then the device complexity remains low, but the adaptability to different histogram shapes deteriorates
Solution Approach 1:
The patent applies local quality by treating different regions of the histogram differently. Instead of using a uniform scanning approach, the control unit adjusts scanning speed based on local characteristics (slope and flatness) of specific histogram regions, allowing adaptive processing without complex global control logic
Solution Approach 2:
The patent changes scanning parameters (speed) based on local histogram characteristics. By calculating slope and flatness values for different histogram regions and adjusting scanning speed accordingly, the system achieves high adaptability to various histogram shapes while maintaining relatively simple control logic
3Productivity
If the scanning speed is increased to reduce processing time, then the productivity improves, but the measurement precision of limit value determination deteriorates
Solution Approach 1:
The patent uses dynamics to adjust scanning speed based on real-time histogram characteristics. The control unit calculates slope and flatness values and dynamically modifies scanning speed, enabling fast processing in suitable regions while maintaining precision in critical regions where accurate limit value determination is essential
Solution Approach 2:
The patent changes the scanning speed parameter based on histogram analysis results. By modifying this parameter adaptively according to calculated characteristics (slope, flatness), the system achieves high productivity while preserving measurement precision through parameter optimization
Data Source
AI summary
In a method for determining upper and lower limit values for a target brightness when image contrast is extended, an upper and lower limit value search processing unit establishes two adjacent areas in accordance with brightness of a grayscale histogram, and, while scanning the positions of those areas, compares the frequency of those areas to a threshold, and if one frequency value is greater than or equal to the threshold value and the other frequency value is lower than the threshold, performs upper and lower limit value search processing wherein a brightness value at the boundary of the two areas is determined as an upper or lower limit value. Thresholds for upper and lower limit value search start position and frequency are established based on the shape of the grayscale histogram of an image to be processed. The shape of the grayscale histogram is identified according to preset classifications.