Iterative reconstruction of industrial positron images with generative networks
Mingwei Zhu, Min Zhao, Min Yao
TL;DR
This paper introduces a new method using generative networks to improve the quality of industrial positron images, especially when data is limited.
Contribution
The novel approach integrates a generative adversarial network with iterative reconstruction and a likelihood-based constraint for better image quality.
Findings
The method shows significant improvements in PSNR and SSIM on simulated datasets.
Real-world industrial defect detection confirms the method's effectiveness.
The self-attention mechanism enhances performance under low-sample conditions.
Abstract
Positron imaging has shown great potential in industrial non-destructive testing due to its high sensitivity and ability to reveal internal structures of complex components. However, reconstructing high-quality images from positron emission data remains challenging, particularly under limited sampling and ill-posed inverse problems, which are common in applications such as closed cavity detection. To address this, we propose an iterative reconstruction method for industrial positron images based on a generative adversarial network (PIIR-GAN). The method integrates a generative adversarial framework with a self-attention mechanism to exploit prior information and improve image quality under low-sample conditions. A key innovation is embedding the neural network model directly into the iterative reconstruction process, enabling end-to-end learning. Furthermore, a likelihood-based…
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Taxonomy
TopicsMuon and positron interactions and applications · Mineral Processing and Grinding · Medical Imaging Techniques and Applications