# CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry

arXiv: https://arxiv.org/abs/1908.09414 · HTML: https://mlsanity.com/paper/1908.09414/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future](https://mlsanity.com/paper/2108.04543/) (2021)
- [Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement](https://mlsanity.com/paper/2105.08040/) (2021)
- [Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising](https://mlsanity.com/paper/2104.08538/) (2021)
- [Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy](https://mlsanity.com/paper/2104.09435/) (2021)
- [Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain](https://mlsanity.com/paper/2103.09022/) (2021)
- [Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks](https://mlsanity.com/paper/2108.02743/) (2021)
- [Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey](https://mlsanity.com/paper/2509.15363/) (2025)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)
- [Unpaired Deep Learning for Pharmacokinetic Parameter Estimation from Dynamic Contrast-Enhanced MRI](https://mlsanity.com/paper/2306.04339/) (2023)
- [Self-Supervised Single-Image Deconvolution with Siamese Neural Networks](https://mlsanity.com/paper/2308.09426/) (2023)
- [Physen-Noise2Noise: Physics-Guided Self-Supervised Defocus Deblurring with Bias Correction under Low-Light Conditions](https://mlsanity.com/paper/2605.24590/) (2026)

## Abstract (arXiv)

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.