Video image fusion performance evaluation method based on three-dimensional Log-Gabor conversion

Abstract

The invention discloses a video image fusion performance evaluation method based on three-dimensional Log-Gabor conversion. The video image fusion performance evaluation method mainly solves the problem that fusion algorithm performance cannot be evaluated accurately in the prior art under the condition that an input video has noise or background movement. The video image fusion performance evaluation method includes: utilizing the three-dimensional Log-Gabor conversion to perform multi-direction multi-scale decomposition on the input video and a fused video; utilizing three-dimensional phase consistency of a video image to build space-time phase consistency evaluation factors; utilizing three-dimensional Log-Gabor conversion coefficient amplitude to build space-time information extraction evaluation factors; combining the space-time phase consistency evaluation factors and the space-time information extraction evaluation factors to build global space-time performance evaluation factors; and evaluating the video fusion algorithm performance according to calculation results of the factors; and designing partial or global parameters according to a human eye vision standard time-critical success factor (ST-CSF) formula and three-dimensional gradient structure tensor. The video image fusion performance evaluation method based on the three-dimensional Log-Gabor conversion can accurately evaluate the fusion algorithm performance under the condition of noise or background movement and can be used for evaluating video image fusion algorithm performance.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a video fusion performance evaluation method, which is used to comprehensively evaluate the performance of various fusion methods from two aspects of time-space information extraction and time-space consistency. technical background [0002] Image fusion technology has been widely used in machine vision, digital cameras, target recognition and other fields. However, most of the current image fusion methods are designed for static image fusion processing, while there are few studies on multi-sensor video image fusion. In practical applications such as security surveillance and target detection and recognition in battlefield environments, video images from multiple sensors often need to be fused. Video image fusion not only needs to meet the basic requirements of general image fusion in terms of spatial performance, that is, after fusion, each frame of video image should retain use...

AI Technical Summary

Problems solved by technology

Qiang Zhang et al. designed a space-time video image fusion performance evaluation method based on structural similarity and human vision, (Qiang Zhang, Long Wang, "Video fusion performance evaluation based on structural similarity and human visual perception", 10thSignal Processing, 2011), this method can evaluate video from two aspects of spatial information extraction and temporal consistency and stability. However, the temporal consistency and stability evaluation factors in this factor are constructed based on frame difference images, which are very sensitive to noise. And the realization process of this evaluation factor is also difficult

[0004] In the two cases where the input video is noisy or the background is moving, none of the above performance evaluation methods can give accurate performance evaluation results

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