Time domain high pass non-uniformity correction method based on gray scale correlation

Abstract

The invention discloses a time domain high pass non-uniformity correction method based on gray scale correlation, relating to the time domain high pass non-uniformity correction method based on gray scale correlation in the infrared imaging technology field and belonging to the infrared imaging technology field. The time domain high pass non-uniformity correction method based on the gray scale correlation comprises steps of using spatial domain low pass filtering result having edge protection as a correction reference source to perform pre-correction on an inputted image, calculating correction offset value of each frame by combining with time domain high pass filtering, changing the mapping relation between the offset value and the grey scale according to the changing volume of the incident radiation in the same position of each frame, removing the ghost during the correction procedure, and improving the infrared imaging quality. The time domain high pass non-uniformity correction method based on the gray scale correlation can reduce the occurrence probabilities of the ghost and the over-correction of the real-time infrared imaging system non-uniformity correction algorism, improves the infrared imaging quality, reduces the calculation quantity and storage space and facilitates the realization of the hardware.

Description

technical field [0001] The invention relates to a time-domain high-pass non-uniformity correction method based on gray-scale correlation, in particular to a gray-scale correlation-based time-domain high-pass non-uniformity correction method used in the field of infrared imaging, and belongs to the technical field of infrared imaging. Background technique [0002] The fixed pattern noise caused by the non-uniformity of infrared imaging devices is a key factor affecting its imaging quality, and it is necessary to introduce the method of non-uniformity correction (Nonuniformity Correction) in the subsequent image processing to eliminate the noise. At present, the non-uniformity correction methods mainly include Calibration Based Non-uniformity Correction (CBNUC) and Scene Based Non-uniformity Correction (SBNUC). [0003] CBNUC needs to add a radiation baffle in front of the detector as a uniform radiation reference source required by the calibration algorithm. However, because ...

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Problems solved by technology

However, these adaptive algorithms are prone to "ghosting", and unreasonable convergence conditions during the calibration process will cause "over-correction"

For example, the neural network algorithm based on the mean value filter is easy to lose details in the filtering window to generate an improper reference source, causing the appearance of "ghosting"; the subsequent neural network algorithm based on bilateral filtering can maintain details to a certain extent to generate a stable reference source, However, in the case of large scene changes in the front and back frames, the correction parameters of the neural network are difficult to update correctly, causing "ghosting"

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