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#Superdraw ct drawing 2018 generator#
Four types of loss functions are presented to build a new one for enforcing the mappings between the generator and discriminator. We also apply a parallel 1 × 1 convolution operation to reduce the dimensionality of each hidden layer’s output. We use a deep unsupervised network of 16 residual blocks to design the generator and build a discriminator based on a supervised network. In this paper, a new semi-supervised generative adversarial network is presented to accurately recover high-resolution CT images from low-resolution counterparts. However, high-resolution images are often limited to access due to CT performance and operation factors. Reconstruction of super-resolution CT images using deep learning requires a large number of high-resolution images.
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