Background clutter quantization method based on compressive sensing

Abstract

The invention discloses a background clutter quantization method based on compressive sensing, which solves the problem of incapability of fully embodying visual perception characteristics of human eyes by the conventional clutter scale. The method comprises the following implementation steps of: dividing a background image into a plurality of small units with the same size so as to form a background matrix; extracting main characteristics of a target vector and the background matrix to obtain a target characteristic vector and a background characteristic matrix; randomly combining the target characteristic and the background characteristic to obtain a target measurement matrix and a sensing matrix respectively; normalizing the target measurement matrix and the sensing matrix; reconstructing sparse signals by the normalized target measurement matrix and the normalized sensing matrix; and taking sum of absolute values of the calculated sparse signals as the background clutter scale of the whole image. By fully utilizing three sensing characteristics while searching via human eyes, the consistence of the predictable target detection probability and the subjective actual target detection probability is improved. The invention can be used for predicating and evaluating the target acquisition performance of a photoelectric imaging system.

Description

technical field [0001] The invention belongs to the technical field of image processing, especially by means of the background clutter quantification method of compressed sensing, which fully embodies the perceptual characteristics of human vision in the target acquisition process, not only can be used for the prediction and evaluation of the target acquisition performance of the photoelectric imaging system, but also has It helps to realize the design and evaluation of image processing algorithms and military camouflage schemes. Background technique [0002] Prediction and evaluation of target acquisition performance is an important content in the field of target detection and recognition. To accurately evaluate and predict the target acquisition performance of the photoelectric imaging system, three important factors must be considered simultaneously: the photoelectric imaging system itself, the atmospheric environment and the target background characteristics. In recent ...

AI Technical Summary

Problems solved by technology

However, the statistical variance scale SV is based on the statistical processing of photoelectric images, without considering the visual perception characteristics of the human eye; the edge probability scale POE only uses background information as a reference, which violates the physical essence of background clutter relative to the target

This makes the clutter scale established by SV and POE unable to reasonably reflect the influence of the background on the target acquisition process, and it is difficult to accurately predict and evaluate the field performance of the optoelectronic imaging system

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