Local Black Point Correction for Aerial Imagery Atmospheric Bias
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Solution Overview
Problem
Aerial and natural imagery often suffers from increased brightness and hue modifications due to atmospheric scattering, which varies with time and viewing angle, requiring effective correction methods that account for physical environment characteristics or do not, such as determining a 'black point' for intensity adjustments.
Innovation Solution
A computer-implemented method and system that define local black points for each pixel in a digital image, adjusting pixel brightness based on these points to correct aberrations, using overlapping regions to reduce computational demands and account for variations in image bias.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single black point is determined for the entire image, then the correction process is simple and fast, but the correction accuracy deteriorates due to non-uniform atmospheric scattering across different regions
Solution Approach 1:
The image is divided into multiple local regions, and a separate black point is determined for each region rather than using a single black point for the entire image. This segmentation allows the correction to account for non-uniform atmospheric scattering across different areas of the image, improving correction accuracy while maintaining reasonable processing efficiency.
Solution Approach 2:
Different regions of the image are treated with different correction parameters (local black points) rather than applying a uniform correction across the entire image. This local quality approach ensures that each region is corrected according to its specific atmospheric conditions, thereby improving overall correction accuracy.
2Measurement precision
If local black points are determined for each pixel, then the correction accuracy is maximized, but the computational complexity increases significantly
Solution Approach 1:
Instead of calculating a unique black point for each pixel, the image is segmented into larger local regions, and one black point is determined per region. This reduces the number of black point calculations from potentially thousands of pixels to a manageable number of regions, significantly lowering computational complexity while still capturing spatial variations in atmospheric scattering.
Solution Approach 2:
Multiple pixels within a local region are grouped together to share a common black point. This merging approach reduces the total number of calculations required while maintaining the benefit of location-specific correction, thereby balancing accuracy with computational efficiency.
Data Source
AI summary
The system and method of use are provided for correcting digital images based on the notions of local black points. The system and method are based on determining local black points in a digital image and correcting the intensities of nearby pixels so as to remove bias in the image introduced by atmospheric conditions. By sampling only localized land imagery the claimed method computes black points that are more robust against the localized effects of atmospheric and land reflection effects than black points computed from wider areas. The result of the method is that images corrected by this method are robust to atmospheric conditions that vary with time and viewing angle.