Computer Science ›› 2021, Vol. 48 ›› Issue (8): 139-144.doi: 10.11896/jsjkx.200500150
• Computer Graphics & Multimedia • Previous Articles Next Articles
GUO Lin1,2,3, LI Chen1, CHEN Chen1, ZHAO Rui1, FAN Shi-lin1, XU Xing-yu1
CLC Number:
[1]ZHANG H,ZHANG L,SHEN H.A blind super-resolution reconstruction method considering image registration errors [J].International Journal of Fuzzy Systems,2015,17(2):353-364. [2]ZHANG K B,GAO X B,TAO D A,et al.Single image super-resolution with non-local means and steering kernel regression[J].IEEE Transactions on Image Processing,2012,21( 11):4544-4556. [3]TIMOFTE R,DE V,GOOL L V.Anchored neighborhood regression for fast example-based super-resolution[C]//Procee-dings of 2013 IEEE International Conference on Computer Vision.Sydney,NSW,Australia:IEEE,2013:1920-1927. [4]TIMOFTE R,DESMET V,VANGOOL L.A+:adjusted an-chored neighborhood regression for fast super-resolution[C]//Proceedings of 12th Asian Conference on Computer Vision.Singapore:Springer,2014:111-126. [5]PELEG T,ELAD M.A statistical prediction model based onsparse representations for single image super-resolution [J].IEEE Transactions on Image Processing,2014,23(6):2569-2582. [6]QIN X J,SHAN Y Y,XIAO J J,et al.Self-learning single image super-resolution reconstruction based on compressive sensing and SVR[J].Computer Science,2017,44(S2):169-174. [7]LI J H,WU Y R,LV J J.Onlinesingle image super-resolution algorithm based on group sparse representation[J].Computer Science,2018,45(4):312-318. [8]THAPA D,RAAHEMIFAR K,BOBIER W R,et al.A perfor-mance comparison among different super-resolution techniques [J].Computers & Electrical Engineering,2016,54:313-329. [9]DONG C,LOY C C,HE K M,et al.Image super-resolutionusing deep convolutional networks[J].IEEE Transaction on Pattern Analysis & Machine Intelligence,2016,38(2):295-307. [10]DONG C,CHEN C L,TANG X.Accelerating the Super-Reso-lution Convolutional Neural Network[C]//European Conference on Computer Vision.Springer,Cham,2016:391-407. [11]KIM J,JUNG K L,KYOUNG M L.Accurate Image Super-Re-solution Using Very Deep Convolutional Networks[J].IEEE Conference on Computer Vision and Pattern Recognition.2016:1646-1654. [12]KIM J,JUNG K L,KYOUNG M L.Deeply-recursive convolutional network for image super-resolution[C]//IEEE Confe-rence on Computer Vision and Pattern Recognition.2016:1637-1645. [13]LAI W S,HUANG J B,NARENDRA A,et al.Deep laplacian pyramid networks for fast and accurate super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition.2017:624-632. [14]QU Y,LIN L,SHEN F,et al.Joint Hierarchical Category Structure Learning and Large-Scale Image Classification[J]IEEE Transactions on Image Processing,2017,26(9):4331-4346. [15]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Ima-ge Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778. [16]SHI W Z,CABALLERO J,HUSZAR F,et al.Real-time single image and video super-resolution using an effcient sub-pixel convolutional neural network[C]//IEEE Conference on Computer Vision and Pattern Recognition.2016:1874-1883. [17]TAI Y,YANG J,LIU X M.Image super-resolution via deep recursive residual network [C]//IEEE Conference on Computer Vision and Pattern Recognition.2017:3147-3155. [18]TAI Y,YANG J,LIU X M.Memnet:A persistent memory network for image restoration[C]//IEEE International Conference on Computer Vision.2017:4539-4547. [19]HU J,LI S,ALBANIE S,et al.Squeeze-and-Excitation Net-works[C]//IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141. [20]HE K,ZHANG X,REN S,et al.Delving deep into rectifiers:Surpassing human-level performance on imagenet classification [C]//Proceedings of the IEEE International Conference on Computer Vision Santiago.Chile,2015:1026-1034. [21]QIN Z S,ZHU L L,ZANG H J.An image super-resolution reconstruction method based on convolutional neural network:China Patent,201910149271.6[P].2019-06-18[2020-09-17]. |
[1] | XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171. |
[2] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
[3] | TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305. |
[4] | WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293. |
[5] | WANG Xin-tong, WANG Xuan, SUN Zhi-xin. Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network [J]. Computer Science, 2022, 49(8): 314-322. |
[6] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[7] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
[8] | 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. |
[9] | HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78. |
[10] | 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. |
[11] | CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126. |
[12] | HOU Yu-tao, ABULIZI Abudukelimu, ABUDUKELIMU Halidanmu. Advances in Chinese Pre-training Models [J]. Computer Science, 2022, 49(7): 148-163. |
[13] | ZHOU Hui, SHI Hao-chen, TU Yao-feng, HUANG Sheng-jun. Robust Deep Neural Network Learning Based on Active Sampling [J]. Computer Science, 2022, 49(7): 164-169. |
[14] | SU Dan-ning, CAO Gui-tao, WANG Yan-nan, WANG Hong, REN He. Survey of Deep Learning for Radar Emitter Identification Based on Small Sample [J]. Computer Science, 2022, 49(7): 226-235. |
[15] | WANG Jun-feng, LIU Fan, YANG Sai, LYU Tan-yue, CHEN Zhi-yu, XU Feng. Dam Crack Detection Based on Multi-source Transfer Learning [J]. Computer Science, 2022, 49(6A): 319-324. |
|