Infrared Camera Gradient Correction via Local Flat Scene Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mass-produced infrared cameras face challenges in adjusting images to compensate for temperature non-uniformity and gradients, which can limit their image quality and dynamic range allocation.
Innovation Solution
An imaging system that acquires locally flat scene data to determine gradients and generates an offset mask to adjust subsequent image frames, reducing or removing gradients by comparing pixel differences within kernels to a threshold and using the offset mask to adjust pixel intensity values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gradient correction methods are used in mass-produced infrared cameras, then temperature non-uniformity can be compensated, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The image frame is divided into multiple kernels (e.g., 4x4 or 8x8 pixel blocks), and each kernel is further divided into sub-kernels for local contrast calculation. This segmentation allows gradient correction to be performed locally rather than globally, reducing computational complexity while maintaining correction effectiveness.
Solution Approach 2:
The patent applies gradient correction only to regions where it is needed (locally flat scenes with low contrast values below a threshold). By using a contrast threshold to identify regions requiring correction, the system avoids unnecessary processing in high-contrast regions, reducing overall system complexity and computational load.
2Measurement precision
If gradient correction is applied to all image data, then temperature non-uniformity is reduced, but dynamic range is wasted on uniform regions
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on local contrast characteristics. Regions with low local contrast (below threshold) undergo gradient correction, while regions with high local contrast preserve their dynamic range. This local differentiation ensures dynamic range is allocated efficiently where needed.
Solution Approach 2:
The system dynamically adjusts the contrast threshold parameter to determine which regions require correction. By changing the threshold parameter, the system can adapt to different scene conditions and optimize the balance between gradient correction and dynamic range preservation without requiring complex hardware modifications.
3Measurement precision
If local contrast calculation is performed on all kernels, then gradient detection accuracy improves, but processing time increases
Solution Approach 1:
The patent calculates local contrast values only for kernels where the contrast is below a specified threshold. Kernels with high contrast values are skipped, as they are unlikely to contain gradient artifacts requiring correction. This partial processing significantly reduces computation time while maintaining detection accuracy for relevant regions.
Solution Approach 2:
By dividing the image into kernels and further into sub-kernels, the patent enables parallel processing of multiple small regions. This segmentation allows the system to process only the necessary sub-kernels (those with low contrast) independently and concurrently, reducing overall processing time while maintaining accurate gradient detection where needed.
Data Source
AI summary
Imaging systems and methods are disclosed that use locally flat scenes to adjust image data. An imaging system includes an array of photodetectors configured to produce an array of intensity values corresponding to light intensity at the photodetectors. The imaging system can be configured to acquire a frame of intensity values, or an image frame, and analyze the image frame to determine if it is locally flat. If the image frame is locally flat, then that image data can be used to determine gradients present in the image frame. An offset mask can be determined from the image data and that offset mask can be used to adjust subsequently acquired image frames to reduce or remove gradients.