Infrared Camera Gradient Correction Using Locally Flat Scene Analysis
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Solution Overview
Problem
Mass-produced infrared cameras face challenges in adjusting images to compensate for temperature non-uniformity and gradients, which can limit their quality and dynamic range, especially in low-cost, consumer-oriented systems.
Innovation Solution
An imaging system that determines if an image frame is locally flat, generates an offset mask based on pixel intensity values, and uses this mask to adjust subsequent frames, reducing or removing gradients and non-uniformities, thereby improving image quality and reserving dynamic range for high-frequency components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional gradient correction methods are used in mass-produced infrared cameras, then temperature non-uniformity can be compensated, but image quality and dynamic range are limited due to system complexity and cost constraints
Solution Approach 1:
The image frame is divided into multiple kernels (e.g., 3x3 pixel blocks), and each kernel is independently analyzed for local contrast. This segmentation allows the system to efficiently identify locally flat regions without complex global analysis, enabling gradient correction in cost-effective mass-produced systems.
Solution Approach 2:
The system uses the image data itself to automatically identify locally flat scenes and generate correction parameters without requiring external calibration targets or complex manual setup. The imaging system self-calibrates by analyzing its own captured images, reducing system complexity while maintaining correction effectiveness.
2Manufacturing precision
If dynamic range is allocated to low-frequency gradient components, then gradient correction is possible, but high-frequency components like edges and shapes lose dynamic range
Solution Approach 1:
The method extracts only the necessary correction information from locally flat regions of the image to create offset mask parameters. By taking out only the gradient compensation data from specific kernel regions rather than processing the entire image, the system corrects gradients while preserving dynamic range for high-frequency components in the final image.
3Manufacturing precision
If complex gradient correction algorithms are implemented, then image quality improves, but processing time and computational resources increase
Solution Approach 1:
The system applies correction only to regions identified as locally flat by analyzing kernel contrast values. Rather than processing the entire image frame with complex algorithms, it performs partial action on specific kernels that meet the locally flat criterion, significantly reducing computational time while maintaining image quality.
Solution Approach 2:
The method changes the parameter threshold for identifying locally flat regions (e.g., contrast threshold of 0.02 or 2% variation). By adjusting this parameter, the system can quickly filter suitable correction regions without complex analysis, enabling real-time processing in mass-produced systems with limited computational resources.
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.