Morphological attribute filtering multimode fusion imaging method and system and medium

Abstract

The invention discloses a morphological attribute filtering multimode fusion imaging method, which comprises the steps of performing morphological attribute filtering operation on a to-be-fused infrared image, solving an adaptive segmentation threshold, and performing binarization to obtain an infrared image weight map; carrying out edge preserving filtering on the infrared image weight map; calculating according to the infrared image weight graph to obtain a visible light image weight graph, and respectively constructing an image pyramid for the visible light image to be fused, the infrared image to be fused, the infrared image weight graph and the visible light image weight graph; fusing the visible light image, the infrared image and the weighted image pyramid to be fused to obtain fusion, and reconstructing to obtain a final fusion result. According to the morphological attribute filtering multi-mode fusion imaging method, quick and stable multi-mode fusion imaging can be performedby utilizing an image processing means, a fusion imaging result can effectively reserve a salient target in an infrared image and edge and texture details in a visible image, Meanwhile, the morphological attribute filtering multi-mode fusion imaging method has the advantages of high calculation efficiency and good universality.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method, system and medium for morphological attribute filtering multi-mode fusion imaging. Background technique [0002] Fusion imaging technology is an image enhancement technology whose purpose is to comprehensively utilize multi-source images acquired by different types of sensors to generate robust and informative fusion images for subsequent processing and decision-making. With the rapid development of sensor technology and the emergence of various complex application requirements, comprehensive information about relevant scenarios is required to enhance the understanding of various conditions. The same type of sensor can only obtain information from one aspect and cannot provide all the necessary information. Therefore, fusion imaging technology is playing an increasingly important role in applications such as computer vision. [0003] The key to an exce...

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Problems solved by technology

Existing image fusion imaging methods can be roughly divided into multi-scale conversion methods, sparse representation methods, subspace methods, saliency map methods, and deep learning methods that have developed rapidly in recent years. They all have their own characteristics, but the calculation efficiency is low. It is difficult to simultaneously preserve the existence of prominent targets in infrared images and the existence of defects such as edges and texture details in visible images, which to a certain extent affects the application of fusion imaging methods in real scenes

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