Two-Point Correction Coefficient Calculation for Infrared Imaging Systems

Principle of Two-Point Correction Coefficient Calculation for Infrared Images

In infrared thermal imaging systems, detector non-uniformity responses require correction processing of raw images. Two-point correction stands as one of the most prevalent non-uniformity correction methods, whose core principle involves calculating gain and offset coefficients using image data captured at two reference temperatures (high and low).

Engineering Implementation Methodology

The data acquisition phase requires capturing 16 high-temperature reference images and 16 low-temperature reference images. Multi-frame averaging is applied to reduce random noise impact. The high temperature is typically selected near the detector's saturation temperature, while the low temperature is chosen close to ambient temperature.

When calculating the average response value for each pixel, first compute the pixel-wise mean across all high-temperature images to generate the high-temperature reference image. Similarly, process the low-temperature image group to obtain the low-temperature reference image.

The correction coefficients consist of two components: Gain coefficients reflect sensitivity variations of detector pixels to temperature changes Offset coefficients represent inherent deviations of each pixel at reference temperatures

In practical applications, the correction coefficients are stored in the device. For each real-time image frame, pixel-level linear transformation operations are performed using the formula: Corrected_Pixel = (Raw_Pixel - Offset) × Gain. This process yields infrared images with significantly improved uniformity.

This method effectively eliminates fixed-pattern noise and enhances image quality. It holds significant application value in security surveillance, industrial inspection, and related fields. Code implementation typically involves matrix operations for efficient pixel-wise calculations, with calibration parameters stored in lookup tables for real-time processing.