Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures - Xiao Hui; Liu Quan; Xu YiBing; Wang Ming; Liu JianTang (Scientific Reports)
Abstract Positron emission tomography (PET) technology, with its advantages of strong γ-photon penetration and results unaffected by temperature or electromagnetic fields, has emerged as a novel non-contact monitoring technique for industrial flow fields under harsh conditions. However, dynamic sampling leads to a severe lack of photon data within individual time frames, resulting in an ill-posed nature of positron image reconstruction, which introduces uncertainty in noise statistical characteristics and degradation in imaging quality. This paper proposes a novel noise-suppressing super-resolution enhancement module for positron flow field imaging. The module, based on convolution and SwinTransformer structures, achieves noise reduction and enhancement of positron images under conditions of severe photon scarcity. Furthermore, a multi-loss fusion performance evaluation system is constructed to extract texture and hierarchical feature information from the images. Experimental results demonstrate that the proposed module effectively reduces image noise while preserving critical information, achieving significant improvements in the quality of generated positron flow field images.