This repository contains the code for a pixel super-resolution methods. For more information, please contact Liheng Bian (bian at bit dot edu dot cn).
In order to increase signal-to-noise ratio in optical imaging, most detectors sacrifice resolution to increase pixel size in a confined area, which impedes further development of high throughput holographic imaging. Although the pixel super-resolution technique (PSR) enables resolution enhancement, it suffers from the trade-off between reconstruction quality and super-resolution ratio. In this work, we report a high-fidelity PSR phase retrieval method with plug-and-play optimization, termed PNP-PSR. It decomposes PSR reconstruction into independent sub-problems based on generalized alternating projection framework. An alternating projection operator and an enhancing neural network are derived to tackle the measurement fidelity and statistical prior regularization, respectively. PNP-PSR incorporates the advantages of individual operators, achieving both high efficiency and noise robustness. Extensive experiments show that PNP-PSR outperforms the existing techniques in both resolution enhancement and noise suppression.
Please clone this repository by Git or download the zip file firstly.
Run main_PSR.m file to obtain the pixel super-resolution results of the compared algorithms (Conv-PSR, SR-SPAR, AS-PSR) and ours (PNPTV-PSR and PNPNet-PSR).
This demo code runs under gaussian noise
For different noise level, undersampling ratio and reslution, please adjustment parameters to acquire better results.
All the experiments are implemented using MATLAB 2019b with an Intel i7-9700 processor at 3.0GHz and 16GB RAM.
Notice that PNP-PSR algorithms require Matconvnet (1.0-beta25), CUDA (10.0) and cuDNN.