Deep learning for blind structured illumination microscopy

Blind-structured illumination microscopy (blind-SIM) enhances the optical resolution without the requirement of nonlinear effects or pre-defined illumination patterns. It is thus advantageous in experimental conditions where toxicity or biological fluctuations are an issue. In this work, we introduce a custom convolutional neural network architecture for blind-SIM: BS-CNN. We show that BS-CNN outperforms other blind-SIM deconvolution algorithms providing a resolution improvement of 2.17 together with a very high Fidelity (artifacts reduction). Furthermore, BS-CNN proves to be robust in cross-database variability: it is trained on synthetically augmented open-source data and evaluated on experiments. This approach paves the way to the employment of CNN-based deconvolution in all scenarios in which a statistical model for the illumination is available while the specific realizations are unknown or noisy.

Automated summary

Blind-structured illumination microscopy (blind-SIM) is enhanced by a custom convolutional neural network architecture (BS-CNN) which outperforms other blind-SIM deconvolution algorithms. It provides a resolution improvement of 2.17 and reduces artifacts. Additionally, BS-CNN is robust in cross-database variability.