Fig. 1

Schematic of PRS-SIM. a Self-supervised training strategy of PRS-SIM. Four matched image groups \({y}_{A}\), \({y}_{B}\), \({y}_{C}\), and \({y}_{D}\) are generated by applying pixel-realignment operation to a noisy low-resolution (LR) raw SIM image group \(y\). Then with conventional SIM algorithm, four super-resolution (SR) images are reconstructed, which are further randomly arranged as the input and target for neural network training. b Inference pipeline of PRS-SIM. The noisy raw SIM image group are firstly reconstructed into a noisy SR image by conventional SIM algorithm. Then by inputting this noisy SR image into the pre-trained PRS-SIM model, the corresponding noise-free SR SIM image will be generated. Scale bar, 2 μm