Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework
Gamma-ray imaging systems are powerful tools in radiographic diagnosis. However, the recorded images suffer from degradations such as noise, blurring, and downsampling, consequently failing to meet high-precision diagnostic requirements. In this paper, we propose a novel single-image super-resolutio...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-03-01
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| Series: | Matter and Radiation at Extremes |
| Online Access: | http://dx.doi.org/10.1063/5.0236541 |
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| _version_ | 1849766854494519296 |
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| author | Guo-Guang Li Liang Sheng Bao-Jun Duan Yang Li Yan Song Zi-Jian Zhu Wei-Peng Yan Dong-Wei Hei Qing-Zi Xing |
| author_facet | Guo-Guang Li Liang Sheng Bao-Jun Duan Yang Li Yan Song Zi-Jian Zhu Wei-Peng Yan Dong-Wei Hei Qing-Zi Xing |
| author_sort | Guo-Guang Li |
| collection | DOAJ |
| description | Gamma-ray imaging systems are powerful tools in radiographic diagnosis. However, the recorded images suffer from degradations such as noise, blurring, and downsampling, consequently failing to meet high-precision diagnostic requirements. In this paper, we propose a novel single-image super-resolution algorithm to enhance the spatial resolution of gamma-ray imaging systems. A mathematical model of the gamma-ray imaging system is established based on maximum a posteriori estimation. Within the plug-and-play framework, the half-quadratic splitting method is employed to decouple the data fidelity term and the regularization term. An image denoiser using convolutional neural networks is adopted as an implicit image prior, referred to as a deep denoiser prior, eliminating the need to explicitly design a regularization term. Furthermore, the impact of the image boundary condition on reconstruction results is considered, and a method for estimating image boundaries is introduced. The results show that the proposed algorithm can effectively addresses boundary artifacts. By increasing the pixel number of the reconstructed images, the proposed algorithm is capable of recovering more details. Notably, in both simulation and real experiments, the proposed algorithm is demonstrated to achieve subpixel resolution, surpassing the Nyquist sampling limit determined by the camera pixel size. |
| format | Article |
| id | doaj-art-e10f2bd3d919428c8d16b7a248874107 |
| institution | DOAJ |
| issn | 2468-080X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | Matter and Radiation at Extremes |
| spelling | doaj-art-e10f2bd3d919428c8d16b7a2488741072025-08-20T03:04:26ZengAIP Publishing LLCMatter and Radiation at Extremes2468-080X2025-03-01102027402027402-1510.1063/5.0236541Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play frameworkGuo-Guang Li0Liang Sheng1Bao-Jun Duan2Yang Li3Yan Song4Zi-Jian Zhu5Wei-Peng Yan6Dong-Wei Hei7Qing-Zi Xing8Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaState Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, ChinaKey Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, ChinaGamma-ray imaging systems are powerful tools in radiographic diagnosis. However, the recorded images suffer from degradations such as noise, blurring, and downsampling, consequently failing to meet high-precision diagnostic requirements. In this paper, we propose a novel single-image super-resolution algorithm to enhance the spatial resolution of gamma-ray imaging systems. A mathematical model of the gamma-ray imaging system is established based on maximum a posteriori estimation. Within the plug-and-play framework, the half-quadratic splitting method is employed to decouple the data fidelity term and the regularization term. An image denoiser using convolutional neural networks is adopted as an implicit image prior, referred to as a deep denoiser prior, eliminating the need to explicitly design a regularization term. Furthermore, the impact of the image boundary condition on reconstruction results is considered, and a method for estimating image boundaries is introduced. The results show that the proposed algorithm can effectively addresses boundary artifacts. By increasing the pixel number of the reconstructed images, the proposed algorithm is capable of recovering more details. Notably, in both simulation and real experiments, the proposed algorithm is demonstrated to achieve subpixel resolution, surpassing the Nyquist sampling limit determined by the camera pixel size.http://dx.doi.org/10.1063/5.0236541 |
| spellingShingle | Guo-Guang Li Liang Sheng Bao-Jun Duan Yang Li Yan Song Zi-Jian Zhu Wei-Peng Yan Dong-Wei Hei Qing-Zi Xing Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework Matter and Radiation at Extremes |
| title | Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework |
| title_full | Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework |
| title_fullStr | Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework |
| title_full_unstemmed | Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework |
| title_short | Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework |
| title_sort | single image super resolution of gamma ray imaging system using deep denoiser prior based on plug and play framework |
| url | http://dx.doi.org/10.1063/5.0236541 |
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