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

A target detection algorithm, two-dimensional maximum entropy technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problems of masking, difficult to extract targets, and many false targets in segmentation results.

Active Publication Date: 2013-07-31
NANJING UNIV OF SCI & TECH
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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, ...

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

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Embodiment Construction

[0033] Such as figure 1 Shown: a kind of improved two-dimensional maximum entropy segmentation night vision image fusion target detection algorithm of the present invention, comprises the following steps:

[0034] Step 1: Improve the establishment method of the two-dimensional histogram, choose a different weight λ, the value of λ is between (0.01, 3), the improved algorithm is effective, and the weight λ is equivalent to the gray scale of the ordinate factor, that is, the original image and the area grayscale enhanced image to construct a two-dimensional histogram. According to the grayscale characteristics of the target in the infrared and low-light images, if the infrared image is segmented, λ takes a value less than 1. If it is for the low-light image For segmentation, λ takes a value greater than 1;

[0035] Step 2: Divide the histogram, and divide the two-dimensional histogram of the image with the two thresholds that the pixel gray level is equal to t and the neighborh...

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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...

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Application Information

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IPC IPC(8): G06T5/40G06T7/20
Inventor 柏连发张毅陈钱顾国华韩静岳江刘颖彬吴经纬
Owner NANJING UNIV OF SCI & TECH
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