Fine Magnification of Solar Small-Scale Structures in NVST High-Resolution Images

Xiaoxiao Wang; Zhenhong Shang; Zhenping Qiang

doi:10.3788/AOS202040.0910002

[1] Rao C H, Zhu L, Zhang L Q et al. Development of solar adaptive optics[J]. Opto-Electronic Engineering, 45, 170733(2018).

[2] Bao H, Rao C H, Tian Y et al. Research progress on adaptive optical image post reconstruction[J]. Opto-Electronic Engineering, 45, 170730(2018).

[3] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Cham: Springer, 184-199(2014).

[4] Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network[M]. //Leibe B, Matas J, Sebe N, et al. Computer Vision-ECCV 2016. Cham: Springer, 391-407(2016).

[5] Shi W, Caballero J, Huszár F et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 26-July 1, 2016, Las Vagas, Nevada. New York: IEEE, 1874-1883(2016).

[6] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, Nevada. New York: IEEE, 1646-1654(2016).

[7] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, Nevada. New York: IEEE, 1637-1645(2016).

[8] Mao X J, Shen C H. -08-30)[2019-12-10][EB/OL]. Yang Y B. Image restoration using convolutional auto-encoders with symmetric skip connections., top/abs/1606, 08921(2016). https://arxiv.xilesou.

[9] Tai Y, Yang J, Liu X M. Image super-resolution via deep recursive residual network. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 3147-3155(2017).

[10] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, Nevada. New York: IEEE, 770-778(2016).

[11] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 2472-2481(2018).

[12] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 4700-4708(2017).

[13] Tai Y, Yang J, Liu X M et al. MemNet: a persistent memory network for image restoration. [C]//The IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice. New York: IEEE, 4539-4547(2017).

[14] Zhang Y L, Tian Y P, Kong Y et al. Residual dense network for image super-resolution. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-23, 2018, Salt Lake City, UT. New York: IEEE, 2472-2481(2018).

[15] Zhang Z F, Wang Z W, Lin Z et al. Image super-resolution by neural texture transfer. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA. New York: IEEE, 7982-7991(2019).

[16] Hu X C, Mu H Y, Zhang X Y et al. Meta-SR: a magnification-arbitrary network for super-resolution. [C]//The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA. New York: IEEE, 1575-1584(2019).

[17] Li H, Zhang H, Guo X L et al. Image restoration after pixel binning in image sensors[J]. Tsinghua Science and Technology, 14, 541-545(2009).

[18] Chen Y C, Liu G Q. The error analysis and correction of NVST's long-term tracking[J]. Astronomical Research & Technology, 13, 205-212(2016).

[19] Wang L K, Xu Z, Jin Z Y et al. The measurement and analysis of the wavelength shift of the FOV of narrow-band high-resolution imaging channel at NVST[J]. Astronomical Research & Technology, 15, 448-455(2018).

[20] Li Y Y, Chen Y C, Yang L et al. A synchronized data acquisition system in the multi-channel observation system on the NVST of the YNAO[J]. Astronomical Research & Technology, 12, 323-330(2015).

[21] Zeiler M D, Taylor G W, Fergus R. Adaptive deconvolutional networks for mid and high level feature learning. [C]//2011 International Conference on Computer Vision, November 6-13, 2011, Barcelona, Spain. New York: IEEE, 2018-2025(2011).

[22] Kingma D P. -01-30)[2019-12-10][EB/OL]. Ba J. Adam: a method for stochastic optimization., top/abs/1412, 6980(2017). https://arxiv.xilesou.

[23] Zhou D W. An edge-directed bicubic interpolation algorithm. [C]//2010 3rd International Congress on Image and Signal Processing, October 16-18, 2010, Yantai, China. New York: IEEE, 1186-1189(2010).

[24] Blu T, Thévenaz P, Unser M. Linear interpolation revitalized[J]. IEEE Transactions on Image Processing, 13, 710-719(2004).

[25] Jiang N, Wang L. Quantum image scaling using nearest neighbor interpolation[J]. Quantum Information Processing, 14, 1559-1571(2015).