Self-similarity enhancement network for  image super-resolution

Wang Ronggui; Lei Hui; Yang Juan; Xue Lixia

doi:10.12086/oee.2022.210382

Abstract

Abstract

Deep convolutional neural networks (DCNN) recently demonstrated high-quality restoration in the single image super-resolution (SISR). However, most of the existing image super-resolution methods only consider making full use of the inherent static characteristics of the training sets, ignoring the internal self-similarity of low-resolution images. In this paper, a self-similarity enhancement network (SSEN) is proposed to address above-mentioned problems. Specifically, we embedded the deformable convolution into the pyramid structure and combined it with the cross-level co-attention to design a module that can fully mine multi-level self-similarity, namely the cross-level feature enhancement module. In addition, we introduce a pooling attention mechanism into the stacked residual dense blocks, which uses a strip pooling to expand the receptive field of the convolutional neural network and establish remote dependencies within the deep features, so that the patches with high similarity in deep features can complement each other. Extensive experiments on five benchmark datasets have shown that the SSEN has a significant improvement in reconstruction effect compared with the existing methods.

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References (39)

References

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- Wang Ronggui, wangrgui@hfut.edu.cn On this Site On Google Scholar
- Lei Hui, 2019170965@hfut.edu.email.com On this Site On Google Scholar
- Corresponding author: Yang Juan, yangjuan6985@163.com On this Site On Google Scholar
  yangjuan6985@163.com
- Xue Lixia, 51003239@qq.com On this Site On Google Scholar

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DOI: 10.12086/oee.2022.210382

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Wang Ronggui, Lei Hui, Yang Juan, Xue Lixia. Self-similarity enhancement network for image super-resolution. Opto-Electronic Engineering 49, 210382 (2022). DOI: 10.12086/oee.2022.210382

Wang Ronggui, Lei Hui, Yang Juan, Xue Lixia. Self-similarity enhancement network for image super-resolution. Opto-Electronic Engineering 49, 210382 (2022). DOI: 10.12086/oee.2022.210382

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- Received Date November 25, 2021
- Revised Date February 20, 2022
- Published Date May 24, 2022
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Scale	Method	Set5	Set14	BSD100	Urban100	Manga109
Scale	Method	PSNR/SSIM	PSNR/SSIM	PSNR/SSIM	PSNR/SSIM	PSNR/SSIM
2×	Bicubic	33.66/0.9299	30.24/0.8688	29.56/0.8431	26.88/0.8409	30.80/0.9339
	SRCNN^[7]	36.66/0.9542	32.45/0.9067	31.36/0.8879	29.50/0.8946	35.60/0.9663
	VDSR^[8]	37.53/0.9590	33.05/0.9130	31.90/0.8960	30.77/0.9140	37.22/0.9750
	M2SR^[23]	38.01/0.9607	33.72/0.9202	32.17/0.8997	32.20/0.9295	38.71/0.9772
	LapSRN^[34]	37.52/0.9591	33.08/0.9130	31.80/0.8950	30.41/0.9100	37.27/0.9740
	PMRN^[35]	38.13/0.9609	33.85/0.9204	32.28/0.9010	32.59/0.9328	38.91/0.9775
	OISR-RK2^[37]	38.12/0.9609	33.80/0.9193	32.26/0.9006	32.48/0.9317	−
	DBPN^[38]	38.09/0.9600	33.85/0.9190	32.27/0.9000	32.55/0.9324	38.89/0.9775
	RDN^[36]	38.24/0.9614	34.01/0.9212	32.34/0.9017	32.89/0.9353	39.18/0.9780
	SSEN(ours)	38.11/0.9609	33.92/0.9204	32.28/0.9011	32.87/0.9351	39.06/0.9778
3×	Bicubic	30.39/0.8682	27.55/0.7742	27.21/0.7385	24.46/0.7349	26.96/0.8546
	SRCNN^[7]	32.75/0.9090	29.28/0.8209	28.41/0.7863	26.24/0.7989	30.59/0.9107
	VDSR^[8]	33.66/0.9213	29.77/0.8314	28.82/0.7976	27.14/0.8279	32.01/0.9310
	M2SR^[23]	34.43/0.9275	30.39/0.8440	29.11/0.8056	28.29/0.8551	33.59/0.9447
	LapSRN^[34]	33.82/0.9227	29.79/0.8320	28.82/0.7973	27.07/0.8272	32.19/0.9334
	PMRN^[35] OISR-RK2^[37]	34.57/0.9280 34.55/0.9282	30.43/0.8444 30.46/0.8443	29.19/0.8075 29.18/0.8075	28.51/0.8601 28.50/0.8597	33.85/0.9465 −
	RDN^[36]	34.71/0.9296	30.57/0.8468	29.26/0.8093	28.80/0.8653	34.13/0.9484
	SSEN(ours)	34.64/0.9289	30.53/0.8462	29.20/0.8079	28.66/0.8635	34.01/0.9474
4×	Bicubic	28.42/0.8104	26.00/0.7027	25.96/0.6675	23.14/0.6577	24.89/0.7866
	SRCNN^[7]	30.48/0.8628	27.50/0.7513	26.90/0.7101	24.52/0.7221	27.58/0.8555
	VDSR^[8]	31.35/0.8838	28.02/0.7680	27.29/0.7260	25.18/0.7540	28.83/0.8870
	M2SR^[23]	32.23/0.8952	28.67/0.7837	27.60/0.7373	26.19/0.7889	30.51/0.9093
	LapSRN^[34]	31.54/0.8850	28.19/0.7720	27.32/0.7270	25.21/0.7551	29.09/0.8900
	PMRN^[35]	32.34/0.8971	28.71/0.7850	27.66/0.7392	26.37/0.7950	30.71/0.9107
	OISR-RK2^[37]	32.32/0.8965	28.72/0.7843	27.66/0.7390	26.37/0.7953	−
	DBPN^[38]	32.47/0.8980	28.82/0.7860	27.72/0.7400	26.38/0.7946	30.91/0.9137
	RDN^[36]	32.47/0.8990	28.81/0.7871	27.72/0.7419	26.61/0.8028	31.00/0.9151
	SSEN(ours)	32.42/0.8982	28.79/0.7864	27.69/0.7400	26.49/0.7993	30.88/0.9132

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Baseline	√	√	√	√
CLFE	×	√	×	√
Cascaded PADB	×	×	√	√
PSNR/dB	32.28	32.35	32.37	32.42
SSIM	0.8962	0.8971	0.8972	0.8982

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模型参数计算量 PSNR/dB SSIM

RDN^[36] 22M 5096G 34.01 0.9212
OISR-RK3^[37] 42M 9657G 33.94 0.9206
DBPN^[38] 10M 2189G 33.85 0.9190
EDSR^[39] 41M 9385G 33.92 0.9195
SSEN 15M 3436G 33.92 0.9204

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[1]	Zhang L, Wu X L. An edge-guided image interpolation algorithm via directional filtering and data fusion[J]. IEEE Trans Image Process, 2006, 15(8): 2226−2238. DOI: 10.1109/TIP.2006.877407 CrossRef Google Scholar
[2]	Li X Y, He H J, Wang R X, et al. Single image superresolution via directional group sparsity and directional features[J]. IEEE Trans Image Process, 2015, 24(9): 2874−2888. DOI: 10.1109/TIP.2015.2432713 CrossRef Google Scholar
[3]	Zhang K B, Gao X B, Tao D C, et al. Single image super-resolution with non-local means and steering kernel regression[J]. IEEE Trans Image Process, 2012, 21(11): 4544−4556. DOI: 10.1109/TIP.2012.2208977 CrossRef Google Scholar
[4]	徐亮, 符冉迪, 金炜, 等. 基于多尺度特征损失函数的图像超分辨率重建[J]. 光电工程, 2019, 46(11): 180419. Xu L, Fu R D, Jin W, et al. Image super-resolution reconstruction based on multi-scale feature loss function[J]. Opto-Electron Eng, 2019, 46(11): 180419. Google Scholar
[5]	Huang J B, Singh A, Ahuja N. Single image super-resolution from transformed self-exemplars[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 5197–5206. Google Scholar
[6]	沈明玉, 俞鹏飞, 汪荣贵, 等. 多路径递归网络结构的单帧图像超分辨率重建[J]. 光电工程, 2019, 46(11): 180489. Shen M Y, Yu P F, Wang R G, et al. Image super-resolution via multi-path recursive convolutional network[J]. Opto-Electron Eng, 2019, 46(11): 180489. Google Scholar
[7]	Dong C, Loy C C, He K M, et al. Learning a deep convolutional network for image super-resolution[C]//Proceedings of the 13th European Conference on Computer Vision, 2014: 184–199. Google Scholar
[8]	Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1646–1654. Google Scholar
[9]	Hui Z, Gao X B, Yang Y C, et al. Lightweight image super-resolution with information multi-distillation network[C]//Proceedings of the 27th ACM International Conference on Multimedia, 2019: 2024–2032. Google Scholar
[10]	Liu S T, Huang D, Wang Y H. Receptive field block net for accurate and fast object detection[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 404–419. Google Scholar
[11]	Dai T, Cai J R, Zhang Y B, et al. Second-order attention network for single image super-resolution[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 11057–11066. Google Scholar
[12]	Mei Y Q, Fan Y C, Zhou Y Q, et al. Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 5689–5698. Google Scholar
[13]	Jaderberg M, Simonyan K, Zisserman A, et al. Spatial transformer networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 2017–2025. Google Scholar
[14]	Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 7132–7141. Google Scholar
[15]	Zhang Y L, Li K P, Li K, et al. Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 294–310. Google Scholar
[16]	Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 3–19. Google Scholar
[17]	Sun K, Zhao Y, Jiang B R, et al. High-resolution representations for labeling pixels and regions[Z]. arXiv: 1904.04514, 2019. https://arxiv.org/abs/1904.04514. https://arxiv.org/abs/1904.04514. " target="_blank">Google Scholar
[18]	Newell A, Yang K Y, Deng J. Stacked hourglass networks for human pose estimation[C]//Proceedings of the 14th European Conference on Computer Vision, 2016: 483–499. Google Scholar
[19]	Ke T W, Maire M, Yu S X. Multigrid neural architectures[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4067–4075. Google Scholar
[20]	Chen Y P, Fan H Q, Xu B, et al. Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision, 2019: 3434–3443. Google Scholar
[21]	Han W, Chang S Y, Liu D, et al. Image super-resolution via dual-state recurrent networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 1654–1663. Google Scholar
[22]	Li J C, Fang F M, Mei K F, et al. Multi-scale residual network for image super-resolution[C]//Proceedings of the 15th European Conference on Computer Vision (ECCV), 2018: 527–542. Google Scholar
[23]	Yang Y, Zhang D Y, Huang S Y, et al. Multilevel and multiscale network for single-image super-resolution[J]. IEEE Signal Process Lett, 2019, 26(12): 1877−1881. DOI: 10.1109/LSP.2019.2952047 CrossRef Google Scholar
[24]	Feng R C, Guan W P, Qiao Y, et al. Exploring multi-scale feature propagation and communication for image super resolution[Z]. arXiv: 2008.00239, 2020. https://arxiv.org/abs/2008.00239v2. https://arxiv.org/abs/2008.00239v2. " target="_blank">Google Scholar
[25]	Dai JF, Qi H Z, Xiong Y W, et al. Deformable convolutional networks[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 764–773. Google Scholar
[26]	Zhu X Z, Hu H, Lin S, et al. Deformable ConvNets V2: more deformable, better results[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 9300–9308. Google Scholar
[27]	Wang X T, Yu K, Wu S X, et al. ESRGAN: enhanced super-resolution generative adversarial networks[C]//Proceedings of 2018 European Conference on Computer Vision, 2018: 63–79. Google Scholar
[28]	Hou Q B, Zhang L, Cheng M M, et al. Strip pooling: rethinking spatial pooling for scene parsing[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 4002–4011. Google Scholar
[29]	Agustsson E, Timofte R. NTIRE 2017 challenge on single image super-resolution: dataset and study[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017: 1122–1131. Google Scholar
[30]	Bevilacqua M, Roumy A, Guillemot C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//Proceedings of the British Machine Vision Conference, 2012. Google Scholar
[31]	Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[C]//Proceedings of the 7th International Conference on Curves and Surfaces, 2010: 711–730. Google Scholar
[32]	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 Eighth IEEE International Conference on Computer Vision, 2001: 416–423. Google Scholar
[33]	Matsui Y, Ito K, Aramaki Y, et al. Sketch-based manga retrieval using manga109 dataset[J]. Multimed Tools Appl, 2017, 76(20): 21811−21838. DOI: 10.1007/s11042-016-4020-z CrossRef Google Scholar
[34]	Lai W S, Huang J B, Ahuja N, et al. Deep laplacian pyramid networks for fast and accurate super-resolution[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 5835–5843. Google Scholar
[35]	Liu Y Q, Zhang X F, Wang S S, et al. Progressive multi-scale residual network for single image super-resolution[Z]. arXiv: 2007.09552, 2020. https://arxiv.org/abs/2007.09552v3. https://arxiv.org/abs/2007.09552v3. " target="_blank">Google Scholar
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Self-similarity enhancement network for image super-resolution

Abstract

Keywords