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Though modern microscopes have an autofocusing system to ensure optimal
focus, out-of-focus images can still occur when cells within the medium are not
all in the same focal plane, affecting the image quality for medical diagnosis
and analysis of diseases. We propose a method that can deblur images as well as
synthesize defocus blur. We train autoencoders with implicit and explicit
regularization techniques to enforce linearity relations among the
representations of different blur levels in the latent space. This allows for
the exploration of different blur levels of an object by linearly
interpolating/extrapolating the latent representations of images taken at
different focal planes. Compared to existing works, we use a simple
architecture to synthesize images with flexible blur levels, leveraging the
linear latent space. Our regularized autoencoders can effectively mimic blur
and deblur, increasing data variety as a data augmentation technique and
improving the quality of microscopic images, which would be beneficial for
further processing and analysis.
