Unsupervised deep learning method for single image super-resolution of the thick pinhole imaging system using deep image prior

Thick pinhole imaging system is widely used for diagnosing intense pulsed radiation sources. However, owing to the trade-off among spatial resolution, field of view (FOV) and signal-to-noise ratio (SNR), the imaging system normally falls short in achieving high-precision spatial diagnosis. In this paper, we propose an unsupervised deep learning method for single image super-resolution (SISR) of the thick pinhole imaging system. The point spread function (PSF) of the imaging system is obtained by analytical calculation and Monte Carlo simulation methods, and the mathematical model of the imaging system is established using a linear equation. To solve the ill-posed inverse problem, we adopt randomly initialized deep convolutional neural networks (DCNNs) as an image prior without pre-training, which is named deep image prior (DIP). The results demonstrate that, by utilizing the SISR technique to increase the number of pixels in reconstructed images, the proposed DIP algorithm can mitigate the spatial resolution degradation caused by an insufficient spatial sampling frequency of the camera. Compared with various classical algorithms, the proposed DIP algorithm exhibits superior capabilities in recovering high-frequency signals and suppressing ringing artifacts. Furthermore, the convergence and robustness of the proposed DIP algorithm under different random seeds and SNR conditions are also verified.