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Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12365)

Abstract

Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this work, we propose a unifying framework of algorithms for Gaussian image deblurring and denoising. These algorithms are based on deep learning techniques for the design of learnable regularizers integrated into the Wiener-Kolmogorov filter. Our extensive experimentation line showcases that the proposed approach achieves a superior quality of image reconstruction and surpasses the solutions that rely either on deep learning or on optimization schemes alone. Augmented with the variance stabilizing transformation, the proposed reconstruction pipeline can also be successfully applied to the problem of Poisson image deblurring, surpassing the state-of-the-art methods. Moreover, several variants of the proposed framework demonstrate competitive performance at low computational complexity, which is of high importance for real-time imaging applications .

Keywords

Deblurring Denoising Learnable regularizers Microscopy deblurring Wiener filter 

Supplementary material

504476_1_En_12_MOESM1_ESM.pdf (27.1 mb)
Supplementary material 1 (pdf 27745 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Skolkovo Institute of Science and TechnologyMoscowRussia
  2. 2.University College LondonLondonUK
  3. 3.Q.bio Inc.Redwood CityUSA

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