Calibration-based scene self-adaption IRFPA heterogeneity correction method

Abstract

The invention discloses a calibration-based scene self-adaption IRFPA (Infrared Focal Plane Array) heterogeneity correction method, belongs to the field of infrared focal plane detection, combines the advantages of small calculation and strong real-time property in a calibration algorithm and the advantage that a scene heterogeneity algorithm can change a correction coefficient in a self-adaption manner, and proposes a calibration-based scene self-adaption IRFPA heterogeneity correction method. The method comprises the following steps: extracting a uniformly radiated infrared template image; calculating a multipoint temperature correction algorithm gain coefficient and a polarization coefficient; calculating the association coefficient between an infrared scene image and the infrared template image; performing heterogeneity correction of real-time scene image. The method has a simple implementation procedure, small calculation, an obvious image correction effect, and application and popularization values.

Description

technical field [0001] The invention relates to the technical field of image detection and processing, in particular to an image correction method in the infrared focal plane detection technology. Background technique [0002] Infrared focal plane array imaging system has become the development trend of infrared thermal imaging technology due to its advantages of high sensitivity, small size, compact structure, long range, good anti-interference, strong ability to penetrate smoke and haze, and can work around the clock. The staring infrared focal plane array has become the mainstream detection device for the development of future infrared thermal imaging systems. However, due to the limitations of material technology and manufacturing level, there is generally non-uniformity among the response characteristics of each detection unit of the infrared focal plane array (IRFPA), which leads to a significant decrease in the temperature resolution and other performance of the infra...

AI Technical Summary

Problems solved by technology

The two-point correction method requires two assumptions: one is that the response of each detector element is linear, this assumption is true in most areas of the detector response, but it is not true when the input is small and close to saturation, so At this time, the detection unit shows a large nonlinearity; the second is that the response of the detection unit has time stability, otherwise, the calibration data will lose its meaning when used

[0005] In practical applications, the response of each detector element is not completely linear, especially when the irradiance varies in a large range, a large error will be introduced, showing a more obvious nonlinearity

Images