To address the issue of low channel estimation accuracy in Reconfigurable Intelligent Surface （RIS） assisted communication systems， a channel estimation scheme based on Channel Denoising Network （CDN） was proposed， which modeled the channel estimation problem as a channel noise elimination problem. Firstly， traditional algorithms were employed to estimate the received pilot signal preliminarily. Then， the estimated signals were input into the channel estimation network to learn noise features and execute denoising， thereby recovering accurate channel coefficients. Finally， to improve the denoising capability of the network， a Weighted Attention Block （WAB） and a Dilated Convolution Block （DCB） were designed to enhance the network's extraction of dominant noise features， and a multi-scale feature fusion module was designed to prevent the loss of shallow features. Simulation results demonstrate that compared with classical DnCNN （Denoising Convolutional Neural Network） and CDRN （Convolutional neural network-based Deep Residual Network） schemes， the proposed scheme reduces the Normalized Mean Square Error （NMSE） by 2.89 dB and 2.01 dB averagely at different Signal-to-Noise Ratios （SNRs）.