Industrial x-ray image enhancement network based on a ray scattering model

X-ray images frequently have low contrast and lost edge features because of the complexity of objects, attenuation of reflected light, and scattering superposition of rays. Image features are frequently lost in traditional enhancement methods. In this paper, we use a ray scattering model to estimate...

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Bibliographic Details
Published in:Applied optics (2004) 2023-07, Vol.62 (20), p.5526
Main Authors: Ding, Xiaxu, Liu, Yi, Yan, Hongxu, Zhang, Pengcheng, Guo, Niu, Gui, Zhiguo
Format: Article
Language:English
Subjects:
Coders
Feature extraction
Image contrast
Image enhancement
Image quality
Modules
Scattering
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Summary:X-ray images frequently have low contrast and lost edge features because of the complexity of objects, attenuation of reflected light, and scattering superposition of rays. Image features are frequently lost in traditional enhancement methods. In this paper, we use a ray scattering model to estimate coarsely clear images and an encoder–decoder network and multi-scale feature extraction module to add multi-scale and detail information to the images. To selectively emphasize useful features, a dual attention module and UnsharpMasking with learnable correction factors are used. The results of the experiments demonstrate that the method may significantly enhance the quality of x-ray images.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.493750