针对已有方法在图像去噪中存在的不足，提出了一种基于双频特征提取的生成对抗网络。在双频特征提取生成对抗网络中，生成器通过八度卷积对局部冗余通道进行多尺度转换，以提取双频特征图像，通道注意力残差模块被用来获取图像关键特征；判别器采用马尔可夫模型，最后的网络层使用LreLu作为激活函数，以解决神经元死亡问题。将双频特征提取生成对抗网络应用于中国江苏省J油田和陕西省S油田的岩心铸体薄片图像（简称岩心图像）噪声去除中，与其他去噪方法EPLL（expected patch log likelihood）、CBDNet（convolutional blind denoising network）和IRCNN（inception-resnet convolutional neural network）相比，它具有更好的应用效果。具体而言，当高斯噪声的标准差分别为15、25、35 dB时，双频特征提取生成对抗网络在J油田小孔细喉型岩心图像上生成的平均PSNR、NRMSE和SSIM分别为30.140 0 dB、0.031 9和0.973 9，均优于EPLL、CBDNet和IRCNN的生成结果；对于其他类型的岩心图像，双频特征提取生成对抗网络也表现出更好的去噪效果。双频特征提取生成对抗网络能够有效地去除岩心图像噪声，为地质研究人员提供有力的技术支持。

Targeting the limitations of existing image denoising methods, GAN-DFFE (generative adversarial network with dual-frequency feature extraction) was proposed. In the GAN-DFFE, the generator employs octave convolution to transform local redundant channels and extract dual-frequency features from images. The channel attention residual module was utilized to capture key features. The discriminator leverages a Markov model, while the final network layer adopts Leaky ReLU as the activation function to address the issue of dead neurons. The GAN-DFFE was applied to the denoising of petrographic thin section images from J Oilfield in Jiangsu Province and S Oilfield in Shaanxi Province. Compared with other denoising methods such as EPLL (expected patch log likelihood), CBDNet (convolutional blind denoising network), and IRCNN (inception-resNet convolutional neural network), it demonstrates superior performance. Specifically, for standard deviation levels of Gaussian noise of 15 dB, 25 dB, and 35 dB, the average PSNR, NRMSE, and SSIM values obtained using the GAN-DFFE on thin section images of small-hole and fine-throat from J Oilfield are 30.140 0 dB, 0.031 9, and 0.973 9, respectively, which outperform those of EPLL, CBDNet, and IRCNN. For other types of thin section images, the GAN-DFFE also exhibits better denoising effects. This method can effectively remove noise from thin section images and provide robust technical support for geological researchers.