基于深层残差编解码的显微ct图像去噪算法

Abstract

Aiming at the problem that noise will be generated during micro -CT scanning in the laboratory, resulting in the decline of CT image quality after reconstruction, this paper proposes a deep multiresidual encodingdecoding convolutional denoising network. This method is based on the original residual encodingdecoding convolutional network.First, we increased the number of convolutional layers and introduced the multiple residuals to realize effective learning of noise distribution characteristics in lablevel micro -CT images. Second, a special mixloss function was designed to strengthen the network's ability to retain image details. Experimental results show that the proposed method has a significant effect on noise suppression and can greatly preserve the structure and feature information of CT images.