Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation

7 Oct 2019 • Ilja Manakov • Markus Rohm • Christoph Kern • Benedikt Schworm • Karsten Kortuem • Volker Tresp

We cast the problem of image denoising as a domain translation problem between high and low noise domains. By modifying the cycleGAN model, we are able to learn a mapping between these domains on unpaired retinal optical coherence tomography images... (read more)

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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