Abstract:Considering the low signal-to-noise ratio (SNR) and image detail loss caused by additive white Gaussian noise (AWGN), an image denoising model based on the convolutional neural network (CNN) with residual dense blocks is proposed on the basis of the existing CNN algorithms. By introducing a multi-stage residual network and dense connections and using the Leaky ReLU activation function on the whole network, the model can better retain the effective information of images and effectively avoid feature loss while removing the noise of different levels of intensity. Compared with the residual learning model of the denoising CNN (DnCNN), the proposed model has an improved peak SNR by about 0.12 dB on the Set12 and Bsd68 test sets and improved structural similarity by about 0.008 6 on average. The test results reveal that the proposed model can fully extract image features, retain image details, and reduce the computational complexity of the network.