Fourier Ptychography With Information Entropy Based No-Reference Image Quality Assessment Learning

We propose a solution to Fourier ptychographic microscopy (FPM) by combining a no-reference image quality assessment module based on information entropy (IENR-IQA) and a physics-based neural network (PbNN) to achieve rapid reconstruction from multiple low-resolution images to highresolution images. This improves the reconstruction and makes it more generalizable and robust than the traditional FPM methods. Existing reconstruction methodologies are susceptible to systematic errors, including pupil aberration and light-emitting diode (LED) positional intensity discrepancies, which profoundly impact reconstruction clarity and color fidelity. In response, a PbNN featuring image quality evaluation is introduced for FPM reconstruction in this paper. A key innovation is incorporating the IENR-IQA module within a PbNN, to facilitate adaptive correction of spatial variations in LED intensity and position. In addition, the IENR-IQA module employs a fully connected layer to rectify pupil aberration. Rigorous simulations and experiments were conducted to validate the proposed method’s effectiveness and robustness. Experimental results demonstrate that the proposed IENR-IQA model can predict image quality well. When combined with PbNN in FPM, the image quality is improved compared to the existing physical neural networks, making the reconstruction results closer to human vision perception. The proposed IENR-IQA PbNN enhances the possibilities for applying the FPM technology in practice.