Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (6): 35-46.doi: 10.6040/j.issn.1672-3961.0.2022.298
• Machine Learning & Data Mining • Previous Articles
WANG Xuqing, WEI Weibo, YANG Guangyu, SONG Jintao, LÜ Ting, PAN Zhenkuan*
CLC Number:
| [1] CHO S, LEE S. Fast motion deblurring[J]. ACM Transactions on Graphics, 2009, 28(5): 1-8. [2] PAN J, SU Z. Fast l0-regularized kernel estimation for robust motion deblurring[J]. IEEE Signal Processing Letters, 2013, 20(9): 841-844. [3] PAN J, HU Z, SU Z, et al. L0-regularized intensity and gradient prior for deblurring text images and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2): 342-355. [4] 唐梦, 彭国华, 郑红婵. 基于正则化方法的图像盲去模糊[J]. 计算机应用研究, 2014, 31(2): 596-599. TANG Meng, PENG Guohua, ZHENG Hongchan. Blind image deblurring based on regularization method[J]. Application Research of Computers, 2014, 31(2): 596-599. [5] PAN J, SUN D, PFISTER H, et al. Deblurring images via dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(10): 2315-2328. [6] CHAKRABARTI A. A neural approach to blind motion deblurring[C] //Proceedings of Computer Vision — ECCV 2016. Amsterdam, Netherlands: Springer, 2016: 221-235. [7] XU X, PAN J, ZHANG Y J, et al. Motion blur kernel estimation via deep learning[J]. IEEE Transactions on Image Processing, 2018, 27(1): 194-205. [8] SCHULER C J, HIRSCH M, HARMELING S, et al. Learning to deblur[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(7): 1439-1451. [9] LIU K H, YEH C H, CHUNG J W, et al. A motion deblur method based on multi-scale high frequency residual image learning[J]. IEEE, 2020, 8: 66025-66036. [10] NAN Y, JI H. Deep learning for handling kernel/model uncertainty in image deconvolution[C] //Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Seattle, USA: IEEE, 2020: 2385-2394. [11] TAO X, GAO H, SHEN X, et al. Scale-recurrent network for deep image deblurring[C] //Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City, USA: IEEE, 2018: 8174-8182. [12] KUPYN O, BUDZAN V, MYKHAILYCH M, et al. DeblurGAN: blind motion deblurring using conditional adversarial networks[C] //Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City, USA: IEEE, 2018: 8183-8192. [13] MONGA V, LI Y, ELDAR Y C. Algorithm unrolling: interpretable, efficient deep learning for signal and image processing[J]. IEEE Signal Processing Magazine, 2021, 38(2): 18-44. [14] ZHANG J, GHANEM B. ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing[C] //Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City, USA: IEEE, 2018: 1828-1837. [15] LI Y, TOFIGHI M, GENG J, et al. Efficient and interpretable deep blind image deblurring via algorithm unrolling[J]. IEEE Transactions on Computational Imaging, 2020, 6: 666-681. [16] WANG Y, YANG J, YIN W, et al. A new alternating minimization algorithm for total variation image reconstruction[J]. SIAM Journal on Imaging Sciences, 2008, 1(3): 248-272. [17] XIN B, WANG Y, GAO W, et al. Maximal sparsity with deep networks[C] //Proceedings of the 30th International Conference on Neural Information Processing Systems(NIPS16). Red Hook, USA: Curran Associates Inc, 2016: 4347-4355. [18] WANG Z, LING Q, HUANG T S. Learning deep l0 encoders[C] //Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence(AAAI16). Phoenix, USA: AAAI, 2016: 2194-2200. [19] MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C] //Proceedings of Eighth IEEE International Conference on Computer Vision(ICCV 2001). Vancouver, Canada, 2001: 416-423. [20] LEVIN A, WEISS Y, DURAND F, et al. Understanding and evaluating blind deconvolution algorithms[C] //Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Miami, USA: IEEE, 2009: 1964-1971. [21] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco: common objects in context[C] //Proceedings of Computer Vision — ECCV 2014. Zurich, Switzerland: Springer, 2014: 740-755. [22] SUN L, CHO S, WANG J, et al. Edge-based blur kernel estimation using patch priors[C] //Proceedings of IEEE International Conference on Computational Photography(ICCP). Cambridge, USA: IEEE, 2013: 1-8. [23] NAH S, KIM T H, LEE K M. Deep multi-scale convolutional neural network for dynamic scene deblurring[C] //Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, USA: IEEE, 2017: 257-265. [24] HU Z, YANG M H. Good regions to deblur[C] //Proceedings of Computer Vision—ECCV 2012. Florence, Italy: Springer Berlin Heidelberg, 2012: 59-72. |
| [1] | Yujiang FAN,Huanhuan HUANG,Jiaxiong DING,Kai LIAO,Binshan YU. Resilience evaluation system of the old community based on cloud model [J]. Journal of Shandong University(Engineering Science), 2023, 53(5): 1-9, 19. |
| [2] | Ying LI,Jiankun WANG. The classification of mild cognitive impairment based on supervised graph regularization and information fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 65-73. |
| [3] | LIU Xing, YANG Lu, HAO Fanchang. Finger vein image retrieval based on multi-feature fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 118-126. |
| [4] | YU Yixuan, YANG Geng, GENG Hua. Multimodal hierarchical keyframe extraction method for continuous combined motion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 42-50. |
| [5] | ZHANG Hao, LI Ziling, LIU Tong, ZHANG Dawei, TAO Jianhua. A technology prediction model based on fuzzy Bayesian networks with sociological factors [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 23-33. |
| [6] | WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao, JIN Di. Poisson-gamma topic model of describing multiple underlying relationships [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 51-60. |
| [7] | YU Mingjun, DIAO Hongjun, LING Xinghong. Online multi-object tracking method based on trajectory mask [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 61-69. |
| [8] | HUANG Huajuan, CHENG Qian, WEI Xiuxi, YU Chuchu. Adaptive crow search algorithm with Jaya algorithm and Gaussian mutation [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 11-22. |
| [9] | LIU Fangxu, WANG Jian, WEI Benzheng. Auxiliary diagnosis algorithm for pediatric pneumonia based on multi-spatial attention [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 135-142. |
| [10] | Yue YUAN,Yanli WANG,Kan LIU. Named entity recognition model based on dilated convolutional block architecture [J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 105-114. |
| [11] | Xiaobin XU,Qi WANG,Bin GAO,Zhiyu SUN,Zhongjun LIANG,Shangguang WANG. Pre-allocation of resources based on trajectory prediction in heterogeneous networks [J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 12-19. |
| [12] | Yinfeng MENG,Qingfang LI. Recognition learning based on multivariate functional principal component representation [J]. Journal of Shandong University(Engineering Science), 2022, 52(3): 1-8. |
| [13] | Xiushan NIE,Yuling MA,Huiyan QIAO,Jie GUO,Chaoran CUI,Zhiyun YU,Xingbo LIU,Yilong YIN. Survey on student academic performance prediction from the perspective of task granularity [J]. Journal of Shandong University(Engineering Science), 2022, 52(2): 1-14. |
| [14] | Tongyu JIANG, Fan CHEN, Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8. |
| [15] | Jun HU,Dongmei YANG,Li LIU,Fujin ZHONG. Cross social network user alignment via fusing node state information [J]. Journal of Shandong University(Engineering Science), 2021, 51(6): 49-58. |
|
||
