Stray Light Compensation Kernels for Real-Time Imaging
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Solution Overview
Problem
Imaging systems suffer from stray light, which results in undesirable effects such as artifacts, noise, and distortion in captured images, and existing methods for stray light mitigation are computationally intensive.
Innovation Solution
The use of moving averages to build kernels for stray light mitigation, which are used as inverse point spread functions to mitigate stray light, allowing for faster computations suitable for real-time or near real-time imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If existing methods for stray light mitigation are used, then stray light is reduced, but computational complexity increases significantly
Solution Approach 1:
The patent transforms the complex stray light mitigation problem into a simpler form by changing the mathematical parameters and operations used. Instead of implementing full deconvolution algorithms, the invention uses pre-computed kernels with simplified convolution operations that maintain effectiveness while dramatically reducing computational complexity
Solution Approach 2:
The patent performs preliminary computation of deconvolution kernels offline or during system initialization, storing them for later use. This preliminary action separates the heavy computational burden from real-time operation, allowing fast processing during actual imaging while maintaining stray light mitigation effectiveness
2Object-affected harmful factors
If existing methods for stray light mitigation are used, then stray light is reduced, but processing time increases
Solution Approach 1:
The patent changes the computational parameters from complex iterative deconvolution to simplified single-pass convolution operations using pre-computed kernels, dramatically reducing processing time while maintaining stray light mitigation performance
Solution Approach 2:
The patent uses pre-computed kernel representations that capture the essential deconvolution behavior without requiring full deconvolution computation during processing. These copied kernel patterns enable fast convolution-based mitigation that approximates the效果 of full deconvolution with fraction of the processing time
Data Source
AI summary
Techniques for facilitating stray light mitigation are provided. In one example, a method includes determining moving averages associated with an image. Each of the moving averages is associated with a respective window size. The method further includes determining a kernel based on the moving averages. The method further includes generating a stray light compensated image based on the image and the kernel. Related devices and systems are also provided.