针对残差算法的残差网络超分辨率重建问题，提出了改进的残差计算的深度复合残差网络模型。在此研究实验中，改进了原有的残差块，能够充分利用到残差块内部的所有卷积层特征信息，提高生成图像的质量；设置了双层复合结构，加深了模型结构的深度，能够强化模型对图像的特征提取，可以提取更多的图像特征；采用迁移学习的方法，在深度网络结构中通过迁移学习增强图像特征信息，使得该模型性能更稳定。通过天宫一号灰度图像的应用实验表明，该研究提出的改进的深度密集残差网络在天宫一号灰度图像超分辨率重建中表现良好，在卫星图像领域具有应用价值和研究意义。

Aiming at the problem of residual network super-resolution reconstruction by residual algorithm, an improved deep composite residual network model for residual calculation was proposed. In this research, the original residual block was improved, which could make full use of all the convolutional layer feature information inside the residual block to improve the quality of the generated image; a double-layer composite structure was set to deepen the depth of the model structure, it could enhance the feature extraction of the image by the model and could extract more image features; the image feature information was enhanced using the method of transfer learning through transfer learning in the deep network structure, making the performanceof the model more stable. The application experiment of the Tiangong-1 grayscale image show that the improved deep residual dense network proposed in this study performs well in the Tiangong-1 grayscale image super-resolution reconstruction, and has application value and research significance in the field of satellite imagery.