Overview of the four proposed prior parametrization models. In (a) the one-shot Wiener filter with learnable kernels is depicted, while in (b) the same filter with predictable kernels is presented. In (c) the scheme incorporating prediction of perpixel regularization kernels into Wiener filter is shown. The proposed iterative scheme which combines Wiener filtering with the approximation of regularization with a CNNis visualized in (d).
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.
Paper
V. Pronina, F. Kokkinos, D.V. Dylov, S. Lefkimmiatis
Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters
