Page 443 - 《软件学报》2026年第5期
P. 443
2322 软件学报 2026 年第 37 卷第 5 期
2019, 30(S1): 94–104 (in Chinese with English abstract). http.//www.jos.org.cn/1000-9825/19010.htm
[12] Zhang XF, Li JJ. Single image de-raining using a recurrent dual-attention-residual ensemble network. Ruan Jian Xue Bao/Journal of
Software, 2021, 32(10): 3283–3292 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6018.htm [doi: 10.13328/j.cnki.
jos.006018]
[13] Chen JB, Xiong BS, Kuang F, Zhang ZZ. Motion deblurring based on deep feature fusion attention and double-scale. Journal of Image
and Graphics, 2023, 28(12): 3731–3743 (in Chinese with English abstract). [doi: 10.11834/jig.220931]
[14] Wu D, Zhao HT, Zheng SB. Motion deblurring method based on DenseNets. Journal of Image and Graphics, 2020, 25(5): 890–899 (in
Chinese with English abstract). [doi: 10.11834/jig.190400]
[15] Jin Y, Huang MJ, Jiang ZW. Image deblurring based on aggregate residual adversary networks. Journal of Computer-aided Design &
Computer Graphics, 2022, 34(1): 84–93 (in Chinese with English abstract). [doi: 10.3724/SP.J.1089.2022.18839]
[16] Tang S, Wan SD, Xie XZ, Yang SL, Huang R, Gu J, Zheng WP. Multi-scale image blind deblurring network for dynamic scenes. Ruan
Jian Xue Bao/Journal of Software, 2022, 33(9): 3498–3511 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6297.
htm [doi: 10.13328/j.cnki.jos.006297]
[17] Liu Y, Fang FM, Wang TT, Li JC, Sheng Y, Zhang GX. Multi-scale grid network for image deblurring with high-frequency guidance.
IEEE Trans. on Multimedia, 2022, 24: 2890–2901. [doi: 10.1109/TMM.2021.3090206]
[18] Ji SW, Lee J, Kim SW, Hong, JP, Baek SJ, Jung SW, Ko SJ. XYDeblur: Divide and conquer for single image deblurring. In: Proc. of the
2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 17400–17409. [doi: 10.1109/
CVPR52688.2022.01690]
[19] Tsai FJ, Peng YT, Lin YY, Tsai, CC, Lin CW. Stripformer: Strip Transformer for fast image deblurring. In: Proc. of the 17th European
Conf. on Computer Vision. Tel Aviv: Springer, 2022. 146–162. [doi: 10.1007/978-3-031-19800-7_9]
[20] Jiang YQ, Zhang CN, Jin S, Liu, J, Wang JP. CLG-INet: Coupled local-global interactive network for image restoration. In: Proc. of the
31st ACM Int’l Conf. on Multimedia. Ottawa: ACM, 2023. 7580–7589. [doi: 10.1145/3581783.3612251]
[21] Nah S, Hyun Kim T, Mu Lee K. Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. of the 2017 IEEE
Conf. on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017. 257–265. [doi: 10.1109/CVPR.2017.35]
[22] Gao HY, Tao X, Shen XY, Jia JY. Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: Proc. of
the 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019. 3843–3851. [doi: 10.1109/CVPR.
2019.00397]
[23] Purohit K, Rajagopalan AN. Region-adaptive dense network for efficient motion deblurring. In: Proc. of the 34th AAAI Conf. on
Artificial Intelligence. New York: AAAI, 2020. 11882–11889. [doi: 10.1609/aaai.v34i07.6862]
[24] Cho SJ, Ji SW, Hong JP, Jung SW, Ko SJ. Rethinking coarse-to-fine approach in single image deblurring. In: Proc. of the 2021
IEEE/CVF Int’l Conf. on Computer Vision. Montreal: IEEE, 2021. 4621–4630. [doi: 10.1109/ICCV48922.2021.00460]
[25] Li HY, Zhang ZR, Jiang TT, Luo P, Feng HJ, Xu ZH. Real-world deep local motion deblurring. In: Proc. of the 37th AAAI Conf. on
Artificial Intelligence. Washington: AAAI, 2023. 1314–1322. [doi: 10.1609/aaai.v37i1.25215]
[26] Zhang YN, Li Q, Qi M, Liu D, Kong J, Wang JZ. Multi-scale frequency separation network for image deblurring. IEEE Trans. on
Circuits and Systems for Video Technology, 2023, 33(10): 5525–5537. [doi: 10.1109/TCSVT.2023.3259393]
[27] Chen LY, Chu XJ, Zhang XY, Sun J. Simple baselines for image restoration. In: Proc. of the 17th European Conf. on Computer Vision.
Tel Aviv: Springer, 2022. 17–33. [doi: 10.1007/978-3-031-20071-7_2]
[28] Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang MH. Restormer: Efficient Transformer for high-resolution image restoration. In:
Proc. of the 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022. 5718–5729. [doi: 10.1109/
CVPR52688.2022.00564]
[29] Kong LS, Dong JX, Ge JJ, Li MQ, Pan JS. Efficient frequency domain-based Transformers for high-quality image deblurring. In: Proc. of
the 2023 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023. 5886–5895. [doi: 10.1109/CVPR52729.
2023.00570]
[30] Tao X, Gao HY, Shen XY, Wang J, Jia JY. Scale-recurrent network for deep image deblurring. In: Proc. of the 2018 IEEE/CVF Conf. on
Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 8174–8182. [doi: 10.1109/CVPR.2018.00853]
[31] Wan SD, Tang S, Xie XZ, Gu J, Huang R, Ma B, Luo L. Deep convolutional-neural-network-based channel attention for single image
dynamic scene blind deblurring. IEEE Trans. on Circuits and Systems for Video Technology, 2021, 31(8): 2994–3009. [doi: 10.1109/
TCSVT.2020.3035664]
[32] Kupyn O, Budzan V, Mykhailych M, Mishkin D, Matas J. DeblurGAN: Blind motion deblurring using conditional adversarial networks.
In: Proc. of the 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018. 8183–8192. [doi: 10.

