Image Non-uniformity Mitigation via SFFC Scaling
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Solution Overview
Problem
Imaging systems face challenges in mitigating image non-uniformities caused by defects in the optical path, which affect the quality of the images produced.
Innovation Solution
The method involves cropping the image and a supplemental flat field correction (SFFC) map based on detected defects, determining a scaling value using a cost function, and applying this scaling value to the SFFC map to obtain a scaled map, which is then applied to the image to correct non-uniformities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire image and SFFC map are processed to correct non-uniformities, then comprehensive correction coverage is achieved, but computational complexity increases
Solution Approach 1:
The patent divides the image and SFFC map into multiple regions based on detected defect locations. Each region is processed independently with appropriate scaling factors, rather than applying uniform processing to the entire image. This segmentation reduces the computational domain while maintaining correction effectiveness in defective areas.
Solution Approach 2:
The patent applies different scaling factors to different regions of the SFFC map based on local defect characteristics. Regions containing defects receive scaled correction with optimized factors, while defect-free regions use standard correction. This local differentiation reduces overall computational complexity by focusing resources where needed.
2Device complexity
If defect regions are isolated and cropped for processing, then computational complexity is reduced, but correction coverage may be limited
Solution Approach 1:
The patent uses scaling factors as intermediary parameters that bridge the cropped defect regions and the full image correction. The scaled SFFC maps from cropped regions are applied to the complete image, allowing localized processing to achieve global correction effects through mathematical interpolation and scaling.
Solution Approach 2:
The patent transitions from spatial cropping (2D reduction) to parameter-space scaling (introducing scaling factors as a new dimension). By optimizing scaling factors for cropped regions and applying them globally, the system achieves comprehensive coverage through parameter transformation rather than purely spatial operations.
Data Source
AI summary
Techniques for facilitating non-uniformity mitigation for imaging systems and methods are provided. In one example, a method includes cropping an image based on a defect in the image to obtain a cropped image. The method further includes cropping a supplemental flat field correction (SFFC) map based on the defect in the image to obtain a cropped SFFC map. The method further includes determining a scaling value of a scaling term based at least on a cost function. The method further includes scaling the SFFC map based on the scaling value to obtain a scaled SFFC map. Related devices and systems are also provided.