A resnet-based universal method for speckle reduction in optical coherence tomography images
This addresses the problem of image quality degradation for medical imaging applications, but it is incremental as it builds on existing ResNet architectures.
The researchers tackled speckle noise reduction in optical coherence tomography (OCT) images by proposing a ResNet-based universal method, achieving over 22 dB signal-to-noise ratio improvement with minimal structure blurring.
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.