High-Dynamic-Range Ptychography Using Maximum Likelihood Noise Estimation

As crucial constraints of ptychography, the richness and accuracy of diffraction patterns directly affect the quality of reconstruction images. This paper proposes a high-dynamic-range ptychography using maximum likelihood noise estimation (ML-HDR). Herein, assuming the linear response of the detector, a compound Gaussian noise model is established; the weight function is optimized according to the ML estimation; and a high signal-to-noise ratio diffraction pattern is further synthesized from multiple low dynamic range diffraction patterns. The reconstruction quality of single exposure, conventional HDR, and ML-HDR is compared. The simulation and experiment results show that ML-HDR can widen the dynamic range by 8 bits and enhance the reconstruction resolution by 2. 83 times compared with the single exposure. Moreover, compared with conventional HDR, ML-HDR can enhance the contrast and uniformity of the reconstruction image in the absence of additional hardware parameters.