  Deconvolution microscopy has been extensively used to improve the resolution
of the wide-field fluorescent microscopy, but the performance of classical
approaches critically depends on the accuracy of a model and optimization
algorithms. Recently, the convolutional neural network (CNN) approaches have
been studied as a fast and high performance alternative. Unfortunately, the CNN
approaches usually require matched high resolution images for supervised
training. In this paper, we present a novel unsupervised cycle-consistent
generative adversarial network (cycleGAN) with a linear blur kernel, which can
be used for both blind- and non-blind image deconvolution. In contrast to the
conventional cycleGAN approaches that require two deep generators, the proposed
cycleGAN approach needs only a single deep generator and a linear blur kernel,
which significantly improves the robustness and efficiency of network training.
We show that the proposed architecture is indeed a dual formulation of an
optimal transport problem that uses a special form of the penalized least
squares cost as a transport cost. Experimental results using simulated and real
experimental data confirm the efficacy of the algorithm.
