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 multi-residual encoding-decoding convolutional denoising network. This method is based on the original residual encoding-decoding convolutional network. First, we increased the number of convolutional layers and introduced the multiple residuals to realize effective learning of noise distribution characteristics in lab-level micro-CT images. Second, a special mix-loss 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.