Improved two-dimensional maximum entropy division night vision image fusion target detection algorithm

Abstract

The invention discloses an improved two-dimensional maximum entropy division night vision image fusion target detection algorithm which comprises the steps that a two-dimensional histogram is improved, that is the two-dimensional histogram is established according to the maximum gray scale of a gray scale-weighted area, a weight is selected, the maximum entropy is calculated by using the histogram, and infrared and low light images are divided. Compared with the traditional maximum entropy division algorithm, an effect in the aspect of target detection is obvious, and background suppression and target extraction functions are provided; and then a phase of the multi-dimensional characteristic and the validation of operation are verified, and the divided infrared and low light images are subjected to characteristic level fusion target detection. The detection algorithm has a better effect and better applicability in the aspects of target detection under a complicated background and multi-target detection.

Description

technical field [0001] The invention belongs to the field of infrared and low-light image processing, in particular to an improved two-dimensional maximum entropy segmentation night vision image fusion target detection algorithm. Background technique [0002] Night vision image target detection technology is developing rapidly in both military and civilian fields. In terms of night vision imaging, single-band images such as infrared band images mainly reflect the difference in thermal radiation between the target object and the scene. The scene elements are not rich enough, the imaging is not clear enough, and it is not conducive to human eyes to distinguish; low-light image contrast is relatively low, The grayscale dynamic range is not large enough, the signal-to-noise ratio is low, and the noise is serious. At this stage, target detection is generally achieved through image segmentation and target extraction. The specific segmentation algorithms mainly include edge detect...

AI Technical Summary

Problems solved by technology

Coupled with a complex background such as a similar thermal radiation background, the results obtained by selecting the maximum entropy segmentation of the traditional two-dimensional histogram often make the target covered by the similar radiation background

For low-light images, the target is generally a darker area or a brighter area. Since the scene elements in the low-light image are richer and the background and noise close to the gray level of the target are more, there are more false targets in the segmentation results, which is difficult. extract target

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