基于生成对抗网络的单帧红外图像超分辨算法
[1] Dong C, Chen C L, He K, et al. Learning a deep convolutional network for image super-resolution[J]. European Conference on Computer Vision, 2014, 8692:184-199.
[2] Dong C, Chen C L, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, 38(2):295.
[3] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C] Computer Vision and Pattern Recognition. IEEE, 2016:1637-1645.
[4] 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] Computer Vision and Pattern Recognition. IEEE, 2016:1874-1883.
[6] DENG C Z, TIAN W, CHEN P, et al. Infrared image super-resolution via locality-constrained group sparse model[J]. Acta Phys Sin(邓承志, 田伟, 陈盼,等. 基于局部约束群稀疏的红外图像超分辨率重建. 物理学报), 2014, 63(4):144-151.
[7] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. Computer Vision and Pattern Recognition. IEEE, 2016: 4681-4690.
[8] Socarras Y, Ramos S, Vazquez D, et al. Adapting pedestrian Detection from synthetic to far infrared images[C]. In ICCV-Workshop on Visual Domain Adaptation and Dataset Bias. Sydney, Australia, 2013.
[9] Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. Computer Science, 2014.
[10] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014,3:2672-2680.
邵保泰, 汤心溢, 金璐, 李争. 基于生成对抗网络的单帧红外图像超分辨算法[J]. 红外与毫米波学报, 2018, 37(4): 427. SHAO Bao-Tai, TANG Xin-Yi, JIN Lu, LI Zheng. Single frame infrared image super-resolution algorithm based on generative adversarial nets[J]. Journal of Infrared and Millimeter Waves, 2018, 37(4): 427.