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-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.