Computational Alleviation of Depth-dependent Degradation in Fluorescence Images
This technology includes an approach that dramatically lessens the effects of depth-dependent degradation in fluorescence microscopy images. First, we develop realistic ‘forward models’ of the depth dependent degradation and apply these forward models to shallow imaging planes that are expected to be relatively free of such degradation. In doing so, we create synthetic image planes that resemble the degradation found in deeper imaging planes. Second, we train neural networks to remove the effect of such degradation, using the shallow images as ground truth.