Adaptive Image Shading Correction via Block Pair Filtering
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Solution Overview
Problem
Conventional image shading correction methods fail to achieve consistent performance across different imaging modules due to luminance and color shading phenomena, leading to errors in shadow compensation, particularly 'metamerism' issues.
Innovation Solution
An adaptive image shading correction method and system that divides frames into blocks, filters block pairs based on brightness, saturation, hue, and sharpness similarities, calculates a sum similarity threshold, and adjusts frames with shading compensation values to generate a compensated frame, utilizing existing statistics for automatic white balance and exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image shading compensation methods are used, then the processing is simple and fast, but the compensation accuracy is poor due to metamerism phenomena and inability to adapt to different imaging modules
Solution Approach 1:
The image frame is divided into multiple blocks, and block pairs are selected for processing. This segmentation allows the system to process only relevant regions with similar characteristics, improving accuracy while controlling complexity through selective processing rather than full-frame analysis.
Solution Approach 2:
Different blocks are processed with different compensation values based on their local characteristics. The system calculates shading compensation values for each block pair and applies them locally, allowing adaptive compensation that accounts for spatial variations in shading and metamerism effects.
2Adaptability or versatility
If a single compensation setting is used for all modules, then the system is simple to operate, but the performance is inconsistent across different lenses and sensors
Solution Approach 1:
The compensation settings are made dynamic and adaptive rather than static. The system automatically adjusts compensation values based on the specific characteristics of each imaging module by analyzing block pairs from captured frames, enabling adaptability without requiring manual reconfiguration for each module.
Solution Approach 2:
The system performs self-calibration by automatically analyzing image blocks and generating appropriate compensation values. This self-service capability allows the system to adapt to different imaging modules without external intervention or complex manual setup, maintaining operational simplicity while achieving module-specific optimization.
3Measurement precision
If additional computations and hardware support are added to improve compensation accuracy, then the precision increases, but the processing time and system complexity increase
Solution Approach 1:
The system applies partial processing by selecting specific block pairs rather than processing the entire frame. This selective approach achieves sufficient compensation accuracy for critical regions while reducing overall processing time and computational load compared to full-frame analysis.
Solution Approach 2:
The system changes processing parameters dynamically by adjusting the number and selection of block pairs based on image characteristics. This allows the system to adapt computation intensity to match the required precision level, optimizing the balance between accuracy and processing time for different scenarios.
Data Source
AI summary
An adaptive image shading correction method and an adaptive image shading system are provided. The method includes: configuring an image capturing device to obtain a current frame; and configuring a processing unit to: divide the current frame into blocks; select block pairs from the blocks, in which each of the block pairs includes an inner block and an outer block; perform a filtering process for each of the block pairs to determine whether a brightness condition, a saturation condition, a hue similarity condition, and a sharpness similarity condition are met; in response to obtaining filtered block pairs, calculate a sum similarity threshold based on hue statistical data, a saturation difference, and a brightness difference; and use filtered blocks with individual thresholds less than the sum similarity threshold to calculate a shadow compensation value to adjust the current frame.