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 net- work (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.

1. 1
CycleGAN with a Blur Kernel for Deconvolution
Microscopy: Optimal Transport Geometry
Theoretical/experimental application of cycleGAN for deconvolution task
Lim et al. , 2020, IEEE TCI

2. 2
Lim et al. , IEEE TCI 2020
CycleGAN for microscopy deconvolution – Reformulation of PSF-convolution
Classical formulation of PSF-convolution
noise
Blurring
PSF
Ground-
truth
observation
Reformulation as unsupervised learning
Classical approach:
uses prior knowledge
(via regularization)
our PLS approach using
an inverse mapping to
define a prior.
Output by ANN
Challenge: we don’t know either H or x
(ill-posed problem).
As GΘ, what’s the optimal mapping between
two domains? -> optimal transport
Reformulated as cycleGAN!
Cycle-consistency loss

3. 3
Lim et al. , IEEE TCI 2020
CycleGAN for microscopy deconvolution – Addition to conventional model
a) blind deconvolution b) non-blind deconvolution

4. 4
Lim et al. , IEEE TCI 2020
CycleGAN for microscopy deconvolution – Simulation test
Does the theory hold up in simulation?
Comparison with SOTA methods

5. 5
Lim et al. , IEEE TCI 2020
CycleGAN for microscopy deconvolution – Simulation test
Does the theory hold up in real-world?
Noise addition test

6. 6
Lim et al. , IEEE TCI 2020
CycleGAN for microscopy deconvolution – Simulation test
Does the theory hold up in real-world?
Real-world data test: monkey kidney fibroblasts
Comparison with SOTA methods
PSF-Generalization tests
Estimated PSFs