A multi-generator generative adversarial network (GAN) learning method for image denoising, which proposes a GAN having three generators. The generator is used for denoising Poisson noise, Gaussian blur noise and distortion noise respectively, so as to improve the quality of a low-dose CT (LDCT) image. The generator uses a residual structure, a mapping short connection used in a residual network can avoid the vanishing gradient problem in a deep neural network, and can accelerate network training. GAN training is always a challenging problem due to an unreasonable metric between a generative distribution and a real distribution; and in the present invention, training can be stably performed by limiting a spectral norm of a weight matrix, enhancing the robustness of a training model.
MULTI-GENERATOR GENERATIVE ADVERSARIAL NETWORK LEARNING METHOD FOR IMAGE DENOISING