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 employed 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.