Fusion Enhancement Of Holographic Penetrating Radar Image And X-ray Backscatter Image

There is an urgent need for non-destructive testing of shallow invisible targets in military,security,archaeological,and municipal construction.Considering the variety of materials that need to be penetrated and targets,holographic penetrating radar and X-ray backscatter technology are used to detect same target at the same time.They differ in principle,and have complementary application scenes.Image fusion technology can be used to synthesize two types of image features,improve detection performance,and integrate target information.This paper mainly studies suitable fusion enhancement methods for holographic penetrating radar images and X-ray backscatter images.The main content of this paper including two topics: the analysis of image fusion capability and the method of image fusion.Firstly,based on the principle of microwave holographic penetrating imaging radar and X-ray backscatter imaging technology,analyzing the types of effective information and interference noise in images,and proposing the requirements of image fusion capability analysis algorithm and image fusion algorithm.Secondly,based on the coincidence degree of the target region,evaluate the image quality,and establish an image interference classification system.The X-ray backscatter image is divided into two types: target is clear or fuzzy.Holographic penetrating radar images are classified according to image interference and target fracture degree.It is divided into three categories: slight,moderate and severe.When the target is blurred or the interference is severe,the image isn't suitable for image fusion.The target region detection of X-ray backscatter image is performed by edge detection based on Canny operator.The radar target area is marked based on principal component analysis and connected domain calibration.13 groups of experimental data under plastic plate,solid wood board and Medium Density Fiberboard are classified.The experimental results show that the classification result of the proposed algorithm is consistent with the subjective classification result.Finally,target region fusion based on Shearlet transformation(TRST)is proposed.In order to reduce the interference and noise of the background region in the source image,the fusion rule of the different region is adopted,and the fusion rules in target region and the background region are different.The improved correlation coefficient,average gradient and other indicators are used to evaluate the readability and contour integrity of the target in the fused image,the degree of electromagnetic property retention of the target and the quantity of the false information introduced.The image fusion capability analysis method and the target region-based fusion enhancement method proposed in this paper can classify images and select different enhancement methods for images with different quality,which can improve image enhancement efficiency,highlight target information and reduce background interference.As experimental targets,the line target and the surface target can be enhanced by TRST,and compared with the other five common fusion algorithms,it can improve the target contour integrity effectively and preserve the target electromagnetic characteristics best,with the least artificial texture.Based on the fusion rules of the target area,the background interference can be removed.It facilitates subsequent target detection and recognition.