News Finding: Deep learning corrects for spatial and spectral distortions in mobile-phone microscopy. You can read our recent work at arXiv:
   
Paper Finding: The results, which were obtained using a single feed-forward algorithm, exhibited important advantages such as the enhancement and restoration of fine spatial features, correction for the colour aberrations, and removal of noise artefacts and warping, introduced by the mobile phone microscope optical hardware/components.
News Context: Deep learning corrects for spatial and spectral distortions in mobile-phone microscopy. You can read our recent work at arXiv:
   
Paper Context: To conclude, we demonstrated the significant enhancement of low-resolution, noisy, distorted images of various specimens acquired by a cost-effective, smartphone-based microscope by using a deep learning approach. The results, which were obtained using a single feed-forward algorithm, exhibited important advantages such as the enhancement and restoration of fine spatial features, correction for the colour aberrations, and removal of noise artefacts and warping, introduced by the mobile phone microscope optical hardware/components. For samples that naturally include height/depth variations, such as Pap smear samples, we also observed the advantage of DOF extension with respect to the images of a benchtop microscope with a higher NA.