Infrared target detection method based on improved Tri edge operator

Abstract

The present invention discloses an infrared target detection method based on an improved Tri-edge operator, which has low complexity and high detection accuracy. The method comprises the following steps: (10) background prediction: predicting to obtain a background prediction image according to a to-be-detected original image; (20) residual image extraction: subtracting the background prediction image from the original image to obtain a residual image; (30) increase of the contrast of the image: after adding the residual image to the original image, doubling the grayscale value to obtain a high-contrast image; (40) image noise suppression: carrying out Wiener filtering to obtain a noise-suppressed image; (50) enhanced image acquisition: superimposing the noise-suppressed image with the original image to obtain an enhanced image; (60) edge image extraction: using the edge extraction operator to extract edge information of the noise-suppressed image; (70) image fusion: fusing the enhanced image and the edge image; and (80) target identification: according to the adaptive threshold, identifying the target to obtain an infrared detection target.

Description

technical field [0001] The invention belongs to the technical field of target detection, in particular to an infrared target detection method based on an improved Tri edge operator with low algorithm complexity and high detection accuracy. Background technique [0002] Infrared target detection is an important part of the image processing field. Because of its wide application in military and civil engineering such as individual combat, target tracking, and security monitoring, it has been valued by research institutions at home and abroad, and it is playing an increasingly critical role. Infrared imaging uses the thermal radiation of the target and background in the scene, so it can penetrate smoke and fog, and has strong anti-interference and environmental adaptability; however, due to the influence of its spectral transmission characteristics, the infrared image has low contrast, large noise, Defects such as poor imaging quality put forward higher requirements for the imp...

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Problems solved by technology

[0004] Although these algorithms have improved the target detection probability to a certain extent and effectively removed background noise, there are still limitations such as the need to establish a priori knowledge model, the algorithm is too computationally intensive to implement, and excessive denoising leads to loss of target information. However, it limits the application and development of infrared target detection

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