**6. Conclusions**

In summary, different from the existing despeckling network, MSR-net proposed in this paper adopts the coarse-to-fine structure and the convolutional long short-term memory unit that can obtain high-quality despeckling SAR images. During research, we find that the weights sharing strategy of convolutional kernels can reduce network parameters and training complexity, and the sub-pixel unit used in this work can reduce up-sampling complexity, improve network efficiency, and shorten the runtime of the network with respect to the transposed convolutional layer. Meanwhile, new design evaluation metrics EFKR and FPKR are introduced herein to evaluate the compatibility of the despeckling algorithms to the optical image processing algorithms. Experimental results show that our MSR-net has excellent despeckling ability and achieves the state-of-the-art results both for simulated and real SAR images with low computational costs, especially in low signal noise ratio cases. The adaptability of optical image processing algorithms to SAR images can be enhanced after despeckling in our network.

**Author Contributions:** All of the authors made significant contributions to the work. Y.Z. and J.S. designed the research and analyzed the results. Y.Z. performed the experiments. Y.Z. and X.Y. wrote the paper. C.W., D.K., S.W., and X.Z. provided suggestions for the preparation and revision of the paper.

**Acknowledgments:** This work was supported in part by the Natural Science Fund of China under Grant 61671113 and the National Key R&D Program of China under Grant 2017YFB0502700, and in part by the Natural Science Fund of China under Grants 61501098, and 61571099.

**Conflicts of Interest:** The authors declare no conflicts of interest.
