Image Brightness Distribution Analysis for Contrast Enhancement
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Solution Overview
Problem
Existing image processing methods can only adjust brightness based on three characteristics: dark, bright, and mid brightness ranges, which are insufficient to cover all image situations, resulting in suboptimal contrast enhancement.
Innovation Solution
An image processing device compares the brightness distribution of an image with multiple predetermined distributions and selects a corresponding brightness adjustment function to adjust pixel values, allowing for more nuanced contrast improvement by dividing the brightness range into multiple regions and applying different adjustment degrees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If only three brightness characteristics (dark, bright, mid brightness) are used for image analysis, then the processing method is simple, but it cannot cover all image situations resulting in suboptimal contrast enhancement
Solution Approach 1:
The brightness range is segmented into multiple regions (not just three) to create more granular brightness distributions. This allows the system to distinguish between different image characteristics more precisely, covering more image situations while maintaining manageable complexity through systematic segmentation
Solution Approach 2:
The system dynamically selects from multiple predetermined brightness distributions based on the actual image characteristics. Instead of using a fixed three-region model, the system adapts to different image types by matching against multiple predefined distribution patterns, improving versatility without permanently increasing system complexity
2Manufacturing precision
If all pixels in a brightness region are adjusted by the same degree, then the processing is simple and fast, but the contrast enhancement is suboptimal
Solution Approach 1:
Different adjustment degrees are applied to different pixels within the same brightness region based on their specific brightness values. This local differentiation allows for more precise contrast enhancement while maintaining a structured approach that doesn't excessively complicate the processing system
Solution Approach 2:
The system changes the adjustment parameter (degree of brightness modification) based on the specific brightness value of each pixel within a region. By varying this parameter dynamically within regions rather than applying a uniform value, the system achieves better contrast precision while using a systematic parameter-change approach
Data Source
AI summary
Brightness values of image frames are collected and counted to form a brightness distribution. The brightness distribution of the image frames is compared with predetermined brightness distributions. Brightness values of all pixels of successive image frames are adjusted according to a result of the comparison, so as to improve image contrast.