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The project proposes a novel automatic Speckle Noise detection and removal method.
The innovation and advantages of our RCU-GAN structure are embodied in the adoption of the end-to-end U-shaped GAN framework for training models to produce realistic-looking data, the addition of conditional variable to guide supervised generation of data, and the application of a bidirectional Convolutional Recurrent Neural Network (CRNN) architectures between the coder and decoder of the Generator. We also describe novel evaluation methods for RCU-GAN, where we generate a synthetic laser speec