Compared with normal dose computed tomography(CT)imaging,low-dose CT imaging can effectively reduce the radiation of X-rays to the body,but the resulting noise will significantly reduce the quality of CT imaging and thus affect the doctor's diagnosis.Because of the single extraction channel,the traditional neural network affects the image feature extraction,which is not conducive to the noise reduction of low-dose CT images.This paper analyzes an image denoising method based on dual attention mechanism and fusion of memory and high frequency features.The experimental results show that,compared with the three typical networks commonly used at present,the model can avoid excessive smoothing of CT images,and can effectively preserve the image texture details.Compared with the ADNet network model,the structure similarity is improved by 0.0055,and the peak signal-to-noise ratio is improved by 0.2707.