Sparse representation-based background clutter quantification method

Abstract

The invention discloses a sparse representation-based background clutter quantification method, which mainly solves the problem that the conventional cluster scale cannot accord with the physical essence of background clutter relativity well and cannot fully reflect human vision property. The method comprises the following implementation steps of: partitioning a gray background image to be quantified into a plurality of small equal-size units and combining the small units into a background matrix; extracting main characteristics of a target vector and the background matrix so as to obtain a target characteristic vector and a background characteristic matrix; normalizing the target characteristic vector and the background characteristic matrix; and calculating the sparsest representations of the normalized target characteristic vector in the normalized background characteristic matrix, wherein the summation of absolute values of the sparsest representations is taken as the background clutter scale of the entire image. Two major characteristics during human eye search are fully utilized, the consistency between a predicted target detection probability and a subjective practical target detection probability is enhanced, and the method can be used for predicting 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, and in particular, a background clutter quantization method based on sparse representation can be used for the prediction and evaluation of the target acquisition performance of the photoelectric imaging system. Background technique [0002] The target acquisition performance of photoelectric imaging system is an important concept in military tasks such as photoelectric countermeasures, reconnaissance, early warning and camouflage. In recent years, with the introduction of new materials and advances in manufacturing processes, photodetectors have reached or approached the background limit, and background factors have become a key factor limiting the performance of photoelectric imaging systems. The use of reasonable and accurate background clutter quantization scales in the target acquisition performance characterization model of the photoelectric imaging system can make its prediction result...

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 the two cannot 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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