Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors

先验概率 残余物 正规化(语言学) 超分辨率 人工智能 计算机科学 图像(数学) 迭代重建 模式识别(心理学) 计算机视觉 算法 数学 贝叶斯概率
作者
Xinyi Zhong,Ningning Liang,Ailong Cai,Xiaohuan Yu,Lei Li,Bin Yan
出处
期刊:Journal of X-ray Science and Technology [IOS Press]
卷期号:31 (2): 319-336 被引量:4
标识
DOI:10.3233/xst-221299
摘要

BACKGROUND: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work. OBJECTIVE: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior. METHODS: The new method reconstructs CT images through a reconstruction model incorporating image gradient L0-norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model. RESULTS: The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors. CONCLUSION: Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution.
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