Composite Convolutional Neural Network for Noise Deduction

Abstract

In order to improve the noise reduction performance and the clarity of denoising images, a composite convolutional neural network composed of the convolutional autoencoder network and the feature reconstruction network is proposed. Multiple convolutional layers are added into the autoencoder to extract the image feature information and improve the denoising performance, and the feature reconstruction network is designed to recover the texture and detail information of the image. The cross-connected structure is used to fuse feature information in the convolutional autoencoder network into the feature reconstruction network. Experimental results show that the proposed method has better noise reduction performance than the existing methods for different noise intensity. More texture and detail information could be retained, and the clearer denoising images could be obtained.