Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
This work addresses image restoration in microscopy for biological research, offering improved performance with low computational complexity for real-time applications, though it appears incremental as it combines existing techniques.
The paper tackles the problem of blur and noise in microscopy images by proposing a deep learning framework integrated with Wiener-Kolmogorov filters for Gaussian and Poisson image deblurring and denoising, achieving superior reconstruction quality and surpassing state-of-the-art methods.
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.