Portable deep learning singlet multi-spectral microscope

Abstract

Compared with monochromatic or RGB imaging technologies, multi-spectral imaging technology is beneficial for obtaining more information details from images of different wavelengths with better contrast. A novel portable and cost-effective multispectral microscope is proposed, whose highlights are firstly imaging by a special designed singlet lens with only controlled rotational symmetric aberrations and then removing the controlled aberrations by deep learning computational imaging method to improve the resolution. Our method helps to reduce the extreme difficulties of singlet lens fabrication due to its simple surface produced by the unnecessarily supreme aberration optimization, while ensuring the resolution. In this manuscript, we introduce singlet lens design methods to connect Zernike polynomial coefficients with singlet lens parameters through wavefront aberrations. Then, the deep learning networks, parameters and working environments are provided to computationally enhance the resolution. By imaging a gold standard resolution pattern and typical bio-samples, experiment results demonstrate that our method and demo-setups achieve multi-spectral microscopy well and cost-effectively. It is believable that our portable deep learning singlet multi-spectral microscope would be an alternate to the conventional approaches and applied in biology, chemistry, environmental science, especially in resource-limited areas.