  In this work we propose a ResNet-based universal method for speckle reduction
in optical coherence tomography (OCT) images. The proposed model contains 3
main modules: Convolution-BN-ReLU, Branch and Residual module. Unlike
traditional algorithms, the model can learn from training data instead of
selecting parameters manually such as noise level. Application of this proposed
method to the OCT images shows a more than 22 dB signal-to-noise ratio
improvement in speckle noise reduction with minimal structure blurring. The
proposed method provides strong generalization ability and can process noisy
other types of OCT images without retraining. It outperforms other filtering
methods in suppressing speckle noises and revealing subtle features.
