With the achievements of deep learning, applications of deep convolutional neural networks for the image denoising problem have been widely studied. However, these methods are typically limited by GPU in terms of network layers and other aspects. This paper proposes a multi-level network that can efficiently utilize GPU memory, named Double Enhanced Residual Network (DERNet), for biological-image denoising. The network consists of two sub-networks, and U-Net inspires the basic structure. For each sub-network, the encoder-decoder hierarchical structure is used for down-scaling and up-scaling feature maps so that GPU can yield large receptive fields. In the encoder process, the convolution layers are used for down-sampling to obtain image information, and residual blocks are superimposed for preliminary feature extraction. In the operation of the decoder, transposed convolution layers have the capability to up-sampling and combine with the Residual Dense Instance Normalization (RDIN) block that we propose, extract deep features and restore image details. Finally, both qualitative experiments and visual effects demonstrate the effectiveness of our proposed algorithm.

随着深度学习的成就，深度卷积神经网络在图像去噪问题上的应用得到了广泛的研究。然而，这些方法在网络层和其他方面通常受到 GPU 的限制。本文提出了一种可以有效利用 GPU 内存的多级网络，称为双增强残差网络 (DERNet)，用于生物图像去噪。该网络由两个子网络组成，U-Net 启发了基本结构。对于每个子网络，编码器-解码器层次结构用于缩小和放大特征图，以便 GPU 可以产生大的感受野。在编码器过程中，卷积层用于下采样得到图像信息，并叠加残差块进行初步特征提取。在解码器的操作中，转置卷积层能够上采样并与我们提出的残差密集实例归一化（RDIN）块相结合，提取深度特征并恢复图像细节。最后，定性实验和视觉效果都证明了我们提出的算法的有效性。