The invention provides a GAN
image denoising algorithm fused with an improved residual network. The GAN
image denoising algorithm comprises: S1, preprocessing a
data set image; S2, extracting features of the noisy image by a generator to generate a de-noised image; S3, judging the input image by the
discriminator, and outputting a judgment result; and S4, performing alternate iteration training on the processes according to a
loss function. Compared with a traditional residual network, the multi-layer residual
feature extraction network creatively used in the invention retains the advantages of the original residual network, that is, the problem of gradient disappearance or explosion caused by a single-stack
convolutional neural network is solved, meanwhile, the multi-layer residual
feature extraction network also realizes the extraction of deep-level features and shallow-level detail feature information of the input picture, and the residual network used in the invention also reduces
model network parameters. According to the invention, the dual-channel
discriminator construction model is used, so that the discrimination capability of the
discriminator can be well improved, the generator G can be well trained, and a picture with a better denoising effect can be generated.