A novel multi-frame wavelet generative adversarial network for scattering reconstruction of structured illumination microscopy

Objective. Structured illumination microscopy (SIM) is widely used in various fields of life science research. In clinical practice, it has low phototoxicity, fast imaging speed and no special fluorescent markers. However, SIM is still affected by the scattering medium of biological tissues, resulting in insufficient resolution of the obtained images, which limits the development of life sciences. A novel multi-frame wavelet generation adversarial network (MWGAN) is proposed to improve the scattering reconstruction capability of SIM. Approach. MWGAN is based on two components derived from the original image. A generative adversarial network constructed by wavelet transform is trained to reconstruct some complex details in the cell structure. Multi-frame adversarial network is used to obtain the inter-frame information of the image and use the complementary information of the before and after frames to improve the quality of the model reconstruction. Results. To demonstrate the robustness of MWGAN, multiple low-quality SIM image datasets are tested. Compared with the state-of-the-art methods, the proposed method achieves superior performance in both of the subjective and objective evaluation. Conclusion. MWGAN is effective for improving the clarity of SIM images. Meanwhile, the SIM images reconstructed by multiple frames improve the reconstruction quality of complex regions and allow clearer and dynamic observation of cellular functions.

Automated summary

In this study, a novel multi-frame wavelet generation adversarial network (MWGAN) is proposed to improve the scattering reconstruction capability of structured illumination microscopy (SIM) images. The proposed method achieves superior performance compared to state-of-the-art methods in both subjective and objective evaluation, as demonstrated through testing multiple low-quality SIM image datasets.