Method for correcting infrared focal plane heterogeneity based on sigma filter

Abstract

The invention discloses a method for correcting the infrared focal plane heterogeneity based on a sigma filter. According to the method, sigma filter with the template size of 5*5 is performed on an image after linearity correction, self-adaptive adjustment of the iteration step length of a correction parameter and detection and replacement of abnormal pixels (bad pixels, impulse noise and the like) are completed when edge-preserving smooth filter is achieved, motion detection is achieved by using a change reference image, and only when the difference between the corrected image and the change reference image is larger than a change threshold value, update of the self-adaptive iteration step length is performed on the heterogeneity correction parameter. The sigma filter is applied to correction of the infrared focal plane heterogeneity, the heterogeneity parameter estimation error is reduced by using the edge preserving characteristic, the 'ghost objects' inhibiting capacity is strengthened, simultaneously, the self-adaptive iteration step length and detection and replacement of the abnormal pixels are achieved through the sigma filter, the computational complexity is low, and the method is suitable for hardware circuits.

Description

technical field [0001] The invention belongs to the field of image detection and processing, and more specifically relates to a method for correcting non-uniformity of an infrared focal plane based on a sigma filter. Background technique [0002] Detector non-uniformity has a serious impact on the performance of many imaging systems, especially infrared imaging systems. In the Focal Plane Array (FPA for short), due to the difference in the manufacturing process of each detector element that constitutes the array, the response characteristics to the same input are not the same, which is called the non-uniformity of the focal plane array. Non-uniformity manifests itself as Fixed Pattern Noise (FPN) overlaid on the actual scene in the output image. FPN seriously affects the performance of applications such as infrared image target detection and recognition, and must be suppressed or eliminated through calibration. Since non-uniformity drifts with time and environmental condit...

AI Technical Summary

Problems solved by technology

However, the filter coefficient of the bilateral filter not only depends on the spatial distance between the neighboring pixel and the current pixel, but also depends on the difference between the neighboring pixel value and the current pixel value, so the computational complexity is high

In addition, this method does not have an iterative step size adaptive adjustment mechanism and a variable gating correction parameter update mechanism, so it has insufficient ability to suppress the "ghost" of long-term static scenes and highlighted targets

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