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Remote sensing image denoising is an important problem in the field of remote sensing research. Considering the problem that the existing denoising algorithms lead to the loss of useful edge information of the image, a remote sensing image denoising method based on improved DnCNN (Denoising Convolutional Neural Network) is proposed. The original image is transformed into different subbands based on wavelet transform, and the network structure automatic search method based on genetic algorithm is used to Denoise the DnCNN network with different subbands with different structures and parameters, which makes the extraction of noise components more targeted. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are used to quantitatively evaluate the experimental results. When the standard deviation is 20, compared with the original DnCNN method, the PSNR value is increased by 3.5%, and the image details are clear. The?experimental?results?show?that?the proposed?method can effectively protect the integrity of edge features and contour structure of remote sensing images.