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

26 Aug 2019 • Sungjun Lim • Hyoungjun Park • Sang-Eun Lee • Sunghoe Chang • Jong Chul Ye

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... (read more)

PDF Abstract

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
Batch Normalization	Normalization
Residual Connection	Skip Connections
PatchGAN	Discriminators
ReLU	Activation Functions
Tanh Activation	Activation Functions
Residual Block	Skip Connection Blocks
Instance Normalization	Normalization
Convolution	Convolutions
Leaky ReLU	Activation Functions
Sigmoid Activation	Activation Functions
GAN Least Squares Loss	Loss Functions
Cycle Consistency Loss	Loss Functions
CycleGAN	Generative Models

METHOD

TYPE

Batch Normalization