Short-wave infrared focal plane non-uniformity correction algorithm based on wavelet transformation histogram

Abstract

The invention discloses a short-wave infrared focal plane non-uniformity correction algorithm based on a wavelet transformation histogram. The short-wave infrared focal plane non-uniformity correction algorithm includes the steps that (1) a response statistic model of a short-wave infrared focal plane array is built; (2) a response density function is represented through the time domain histogram of the short-wave infrared focal plane array; (3) the response density function of a single detecting unit is specified to an intermediate value of the response density function of a neighborhood detecting unit with the single-scale intermediate-value histogram specification non-uniformity correction algorithm, and non-uniformity correction is accordingly achieved; (4) multi-scale correction based on orthogonal wavelet transformation resolving is carried out on an original image of the short-wave infrared focal plane array.

Description

technical field [0001] The invention relates to the technical field of short-wave infrared focal planes, in particular to a non-uniformity correction algorithm of short-wave infrared focal plane arrays. Background technique [0002] Aiming at the non-uniformity of SWIR focal plane array, the correction methods proposed in the existing literature are mainly divided into two categories. One is the calibration-based calibration method, which has a simple principle, is easy to implement and integrate hardware, and has high calibration accuracy. It can be used to measure the scene temperature and does not have any requirements for the target. However, this type of method requires a reference source for actual calibration. Calibration makes the equipment relatively complicated. The other is the scene-based method, which directly utilizes the scene information of each frame image for non-uniformity correction, which overcomes the shortcomings of the first method, so it is a hot re...

AI Technical Summary

Problems solved by technology

However, most of the algorithms in this type of technology need to estimate the real scene value. A typical example is: Scribner et al. proposed a non-uniform correction algorithm based on neural network, which uses the average of the four neighborhoods of the noise image as the estimated value of the real image. And it is used to train the neural network, but the premise is that the fixed pa...

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