Computer Science ›› 2021, Vol. 48 ›› Issue (4): 164-168.doi: 10.11896/jsjkx.200100099
Special Issue: Medical Imaging
• Computer Graphics & Multimedia • Previous Articles Next Articles
WANG Wei, HU Tao, LI Xin-wei, SHEN Si-wan, JIANG Xiao-ming, LIU Jun-yuan
CLC Number:
[1]WANG P,DALLA M M,CHANUSSOT J,et al.Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,12(1):334-344. [2]WANG R,ZHANG Y,ZHANG J,et al.Face super-resolution reconstruction based on convolutional neural network[J].Computer Engineering and Design,2019,40(9):2614-2619. [3]ISAAC J S,KULKARNI R.Super resolution techniques formedical image processing[C]//2015 International Conference on Technologies for Sustainable Development(ICTSD).IEEE,2015:1-6. [4]SU H,ZHOU J,ZHANG Z H.Survey of super-resolution imagereconstruction methods[J].Acta Automatica Sinica,2013,39(8):1202-1213. [5]GAO Y,LIU Z,QIN P L,et al.Medical image super-resolution algorithm based on deep residual generative adversarial network[J].Journal of Computer Applications,2018,38(9):2689-2695. [6]XIE T.Super-resolution image restoration via improved POCS algorithm[J].Electronic Design Engineering,2013,21(18):142-144. [7]TAO Z Q,LI H L,ZHANG H B.Iterative Back Projection Super Resolution Reconstruction Algorithm Based on New Edge Directed Interpolation[J].Computer Engineering,2016,42(6):255-260. [8]ZENG K,DING S F.Advances in image super-resolution reconstruction[J].Computer Engineering and Applications,2017,53(16):29-35. [9]DONG C,LOY C C,HE K,et al.Learning a Deep Convolutional Network for Image Super-Resolution[C]//European Conference on Computer Vision.2014:184-199. [10]DONG C,LOY C C,TANG X.Accelerating the Super-Resolution Convolutional Neural Network[C]//European Conference on Computer Vision.2016:391-407. [11]KIM J,KWON L J,MU L K.Deeply-recursive convolutionalnetwork for image super resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1637-1645. [12]LEDIG C,THEIS L,HUSZÁR F,et al.Photo-realistic singleimage super-resolution using a generative adversarial network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:4681-4690. [13]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680. [14]ZHANG H,XU T,LI H S.Stackgan:Text to photo-realisticimage synthesis with stacked generative adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:5907-5915. [15]ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2223-2232. [16]MA L,JIA X,SUN Q,et al.Pose guided person image generation[C]//Advances in Neural Information Processing Systems.2017:406-416. [17]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [18]WANG X,YU K,WU S,et al.Esrgan:Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018. [19]NAH S,KIM T H,LEE K M.Deep multi-scale convolutionalneural network for dynamic scene deblurring[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:3883-3891. [20]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014. [21]JOLICOEUR-MARTINEAU A.The relativistic discriminator:a key element missing from standard GAN[J].arXiv:1807.00734,2018. [22]SHI W,CABALLERO J,FERENC H,et al.Real-time singleimage and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1874-1883. [23]TONG Y B,ZHANG Q S,QI Y P.Image quality assessing by combining PSNR with SSIM[J].Journal of Image and Graphics,2006,12:1758-1763. |
[1] | ZHANG Jia, DONG Shou-bin. Cross-domain Recommendation Based on Review Aspect-level User Preference Transfer [J]. Computer Science, 2022, 49(9): 41-47. |
[2] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
[3] | DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119. |
[4] | XU Guo-ning, CHEN Yi-peng, CHEN Yi-ming, CHEN Jin-yin, WEN Hao. Data Debiasing Method Based on Constrained Optimized Generative Adversarial Networks [J]. Computer Science, 2022, 49(6A): 184-190. |
[5] | YIN Wen-bing, GAO Ge, ZENG Bang, WANG Xiao, CHEN Yi. Speech Enhancement Based on Time-Frequency Domain GAN [J]. Computer Science, 2022, 49(6): 187-192. |
[6] | XU Hui, KANG Jin-meng, ZHANG Jia-wan. Digital Mural Inpainting Method Based on Feature Perception [J]. Computer Science, 2022, 49(6): 217-223. |
[7] | GAO Zhi-yu, WANG Tian-jing, WANG Yue, SHEN Hang, BAI Guang-wei. Traffic Prediction Method for 5G Network Based on Generative Adversarial Network [J]. Computer Science, 2022, 49(4): 321-328. |
[8] | DOU Zhi, WANG Ning, WANG Shi-jie, WANG Zhi-hui, LI Hao-jie. Sketch Colorization Method with Drawing Prior [J]. Computer Science, 2022, 49(4): 195-202. |
[9] | LI Si-quan, WAN Yong-jing, JIANG Cui-ling. Multiple Fundamental Frequency Estimation Algorithm Based on Generative Adversarial Networks for Image Removal [J]. Computer Science, 2022, 49(3): 179-184. |
[10] | ZHOU Ying, CHANG Ming-xin, YE Hong, ZHANG Yan. Super Resolution Reconstruction Method of Solar Panel Defect Images Based on Meta-transfer [J]. Computer Science, 2022, 49(3): 185-191. |
[11] | SHI Da, LU Tian-liang, DU Yan-hui, ZHANG Jian-ling, BAO Yu-xuan. Generation Model of Gender-forged Face Image Based on Improved CycleGAN [J]. Computer Science, 2022, 49(2): 31-39. |
[12] | LI Jian, GUO Yan-ming, YU Tian-yuan, WU Yu-lun, WANG Xiang-han, LAO Song-yang. Multi-target Category Adversarial Example Generating Algorithm Based on GAN [J]. Computer Science, 2022, 49(2): 83-91. |
[13] | TAN Xin-yue, HE Xiao-hai, WANG Zheng-yong, LUO Xiao-dong, QING Lin-bo. Text-to-Image Generation Technology Based on Transformer Cross Attention [J]. Computer Science, 2022, 49(2): 107-115. |
[14] | CHEN Gui-qiang, HE Jun. Study on Super-resolution Reconstruction Algorithm of Remote Sensing Images in Natural Scene [J]. Computer Science, 2022, 49(2): 116-122. |
[15] | LENG Jia-xu, WANG Jia, MO Meng-jing-cheng, CHEN Tai-yue, GAO Xin-bo. Survey on Video Super-resolution Based on Deep Learning [J]. Computer Science, 2022, 49(2): 123-133. |
|