**6. Conclusions**

To see in the extreme dark, we have proposed a new method, NL2LL (collecting a low-light dataset in normal-light condition) to collect image pairs. The method has many potential implementations in convolutional neural network, dilated convolutional NN, regression, and graphical models. Our end-to-end approach is simple and highly effective. We have demonstrated its efficacy in low-light image restoration. The experiment shows that our approach can achieve inspiring results by only using 20 image pairs.

**Author Contributions:** All authors contributed to the paper. H.W. performed project administration; Y.X. conceived, designed and performed the experiments; Y.X. and G.D.C. wrote and reviewed the paper; S.R. performed the experiments; W.S. analyzed the data. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Science Foundation of China (NSFC) grant No. 61571369. It was also funded by Zhejiang Provincial Natural Science Foundation (ZJNSF) grant No.LY18F010018. It was also supported by the 111 Project under Grant No. B18041.

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