Abstract
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. The code is available at https://github.com/nis-research/linear-latent-blur.
I. Mazilu and S. Wang—Contributed equally to this work.
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Acknowledgement
This work was supported by the SEARCH project, UT Theme Call 2020, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente.
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Mazilu, I., Wang, S., Dummer, S., Veldhuis, R., Brune, C., Strisciuglio, N. (2023). Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_20
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DOI: https://doi.org/10.1007/978-3-031-44240-7_20
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