Research Article
A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network
Algorithm 1
The training process of the proposed medical image denoising method based on conditional generative adversarial network.
1.Require: Set hyper-parameters: , batch size =1, , | 2.Get by adding some artificial noise in raw image | 3.Obtain the corresponding gradient map by calculating the gradient for each pixel in | 4.The median T of the gradient map is set as the threshold in , then obtain | 5.Get the gradient enhancement images by adding up the and | 6.Initialize the parameters of generator and discriminator | 7.fordo | 8.Sample a batch of raw image patches and the image to be processed patches | 9. | 10. Concatenate | 11. Update the discriminator D by Adam optimizer according to the original GAN loss | 12. Update the generator G by Adam optimizer according to the Equation (12) | 13.end for |
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