[1] |
Yu Y L, Li X Z, Liu F X. E-DBPN:Enhanced deep back- projection networks for remote sensing scene image superresolution[J].IEEE Transactions on Geoscience and Remote Sensing,2020,58(8):5503-5515.
|
[2] |
Chen L,Pan J,Li Q.Robust face image super-resolution via joint learning of subdivided contextual model[J].IEEE Transactions on Image Processing,2019,28(12):5897-5909.
|
[3] |
You C Y,Li G,Zhang Y,et al.CT super-resolution GAN constrained by the identical,residual,and cycle learning ensemble [J]. IEEE Transactions on Medical Imaging,2020,39(1):188-203.
|
[4] |
Singh A,Singh J.Content adaptive single image interpolation based super resolution of compressed images[J].International Journal of Electrical and Computer Engineering,2020,10(3):3014-3021.
|
[5] |
Ma X, Zhang J, Li T, et al. Super-resolution geomagnetic reference map reconstruction based on dictionary learning and sparse representation[J].IEEE Access,2020,8:84316-84325.
|
[6] |
Kong X T,Zhao H Y,Qiao Y,et al.ClassSR:A general framework to accelerate super-resolution networks by data characteristic[C]∥Proc of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2021:12011-12020.
|
[7] |
Dong C, Loy C C,He K M,et al.Image super-resolution using deep convolutional networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(2):295-307.
|
[8] |
Kim J, Lee J K,Lee K M.Accurate image super-resolution using very deep convolutional networks[C]∥Proc of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:1646-1654.
|
[9] |
He K N,Zhang X Y,Ren S Q,et al.Deep residual learning for image recognition[C]∥Proc of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:770-778.
|
[10] |
Kim J,Lee J K,Lee K M.Deeply-recursive convolutional network for image super-resolution[C]∥Proc of 2016 IEEE Conference on Computer Vision and Pattern Recognition,2016:1637-1645.
|
[11] |
Shi W,Caballero J,Huszar F,et al.Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]∥Proc of 2016 IEEE Confe- rence on Computer Vision and Pattern Recognition,2016:1871-1883.
|
[12] |
Zhang Y L,Li K P,Li K,et al.Image super-resolution using very deep residual channel attention networks[C]∥Proc of the 15th European Conference on Computer Vision,2018:294-310.
|
[13] |
Goodfellow I,Pouget-Abadie J,Mirza M,et al.Generative adversarial nets[C]∥Proc of the 27th International Confe- rence on Neural Information Processing Systems,2014:2672-2680.
|
[14] |
Ledig C,Theis L,Huszár F,et al.Photo-realistic single image super-resolution using a generative adversarial network[C]∥Proc of 2017 IEEE Conference on Computer Vision and Pattern Recognition,2017:105-114.
|
[15] |
Wang X T, Yu K,Wu S X,et al. ESRGAN:Enhanced super-resolution generative adversarial networks[C]∥Proc of the 15th European Conference on Computer Vision Workshop,2018:63-79.
|
[16] |
Li W,Zhou K,Qi L,et al.Best-buddy GANs for highly detailed image super-resolution[J].arXiv:2103.15295,2021.
|
[17] |
Yan Y T,Liu C C,Chen C Y,et al.Fine-grained attention and feature-sharing generative adversarial networks for single image super-resolution[J].IEEE Transactions on Multimedia,2022,24:1473-1487.
|
[18] |
Wang X,Xie L,Dong C,et al.Real-ESRGAN:Training real-world blind super-resolution with pure synthetic data[C]∥Proc of 2021 IEEE/CVF International Conference on Computer Vision Workshops,2021:1905-1914.
|
[19] |
Hou Q, Zhou D,Feng J.Coordinate attention for efficient mobile network design[J].arXiv:2103.02907,2021.
|
[20] |
Kong X T,Liu X N,Gu J J,et al.Reflash dropout in image super-resolution[C]∥Proc of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2022:5992-6002.
|
[21] |
Hinton G,Srivastava N,Krizhevsky A,et al.Improving neural networks by preventing co-adaptation of feature detectors[J].arXiv:1207.0580,2012.
|
[22] |
Schnfeld E,Schiele B,Khoreva A.A U-Net based discriminator for generative adversarial networks[C]∥Proc of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020:8204-8213.
|
[23] |
Arjovsky M,Chintala S,Bottou L,et al.Wasserstein generative adversarial networks[C]∥Proc of the 34th International Conference on Machine Learning,2017:214-223.
|
[24] |
Miyato T,Kataoka T,Koyama M,et al.Spectral normalization for generative adversarial networks[J].arXiv:1802.05957,2018.
|
[25] |
Barron J T.A general and adaptive robust loss function[C]∥Proc of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition,2019:4326-4334.
|
[26] |
Timofte R,Agustsson E, van Gool L,et al.NTIRE 2017 challenge on single image super-resolution:Methods and results[C]∥Proc of 2017 IEEE Conference on Computer Vision and Pattern Recognition,2017:1110-1121.
|