基于密集残差和质量评估引导的频率分离生成对抗超分辨率重构网络

韩玉兰; 崔玉杰; 罗轶宏; 兰朝凤

doi:10.11999/JEIT240388

基于密集残差和质量评估引导的频率分离生成对抗超分辨率重构网络

doi: 10.11999/JEIT240388

1.
哈尔滨理工大学测控技术与通信工程学院哈尔滨 150080
2.
哈尔滨理工大学模式识别与信息感知黑龙江省重点实验室哈尔滨 150080

基金项目: 国家自然科学基金(11804068)，黑龙江省省属高等学校基本科研业务(2020-KYYWF-0342)

详细信息

作者简介:
韩玉兰：女，讲师，研究方向为人工智能与计算机视觉、大数据分析与预测等

崔玉杰：女，硕士，研究方向为图像重构

罗轶宏：女，硕士，研究方向为图像重构

兰朝凤：女，副教授，研究方向为语音信号处理与分析、水下信号分析与处理等

通讯作者:
韩玉兰　hanyulan@hrbust.edu.cn

中图分类号: TN911.73; TP391
计量
- 文章访问数: 20
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- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-16
- 修回日期: 2024-11-11
- 网络出版日期: 2024-11-18

Frequency Separation Generative Adversarial Super-resolution Network Based on Dense Residual and Quality Assessment

1.
School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
2.
Heilongjiang Province Key Laboratory of Pattern Recognition and Information Perception, Harbin University of Science and Technology, Harbin 150080, China

Funds: The National Natural Science Foundation of China (11804068), The Fundamental Research Funds for the Provincial Universities (2020-KYYWF-0342)

摘要

摘要: 生成对抗网络因其为盲超分辨率重构提供了新的思路而备受关注。针对现有方法未充分考虑图像退化过程中的低频保留特性而对高低频成分采用相同的处理方式，缺乏对频率细节有效利用，难以获得较好重构效果的问题，该文提出一种基于密集残差和质量评估引导的频率分离生成对抗超分辨率重构网络。该网络采用频率分离思想，对图像的高频和低频信息分开处理，从而提高高频信息捕捉能力，简化低频特征处理。该文对生成器中的基础块进行设计，将空间特征变换层融入密集宽激活残差中，增强深层特征表征能力的同时对局部信息差异化处理。此外，利用视觉几何组网络(VGG)设计了专门针对超分辨率重构图像的无参考质量评估网络，为重构网络提供全新的质量评估损失，进一步提高重构图像的视觉效果。实验结果表明，同当前先进的同类方法比，该方法在多个数据集上具有更佳的重构效果。由此表明，采用频率分离思想的生成对抗网络进行超分辨率重构，可以有效利用图像频率成分，提高重构效果。
- 超分辨率 /
- 生成对抗网络 /
- 频率分离 /
- 质量评估 /
- 密集残差
Abstract: With generative adversarial networks have attracted much attention because they provide new ideas for blind super-resolution reconstruction. Considering the problem that the existing methods do not fully consider the low-frequency retention characteristics during image degradation, but use the same processing method for high and low-frequency components, which lacks the effective use of frequency details and is difficult to obtain better reconstruction results, a frequency separation generative adversarial super-resolution reconstruction network based on dense residuals and quality assessment is proposed. The idea of frequency separation is adopted by the network to process the high-frequency and low-frequency information of the image separately, so as to improve the ability of capturing high-frequency information and simplify the processing of low-frequency features. The base block in the generator is designed to integrate the spatial feature transformation layer into the dense wide activation residuals, which enhances the ability of deep feature representation while differentiating the local information. In addition, no-reference quality assessment network is designed specifically for super-resolution reconstructed images using Visual Geometry Group (VGG), which provides a new quality assessment loss for the reconstruction network and further improves the visual effect of reconstructed images. The experimental results show that the method has better reconstruction effect on multiple datasets than the current state-of-the-art similar methods. It is thus shown that super-resolution reconstruction using generative adversarial networks with the idea of frequency separation can effectively utilize the image frequency components and improve the reconstruction effect.
- Super-resolution /
- Generative adversarial network /
- Frequency separation /
- Quality assessment /
- Dense residual

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图 1 DR-QA-FSGAN网络总体结构

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图 2 生成器

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图 3 带SFT层的密集宽激活残差块DWRB

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图 4 质量评估网络

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图 5 不同方法在BSDS100数据集“69015”图像4倍超分辨率重构比较

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图 6 不同方法在自制数据集“02”图像4倍超分辨率重构比较

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图 7 不同滤波器在Set5数据集“baby”图像4倍超分辨率重构比较

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图 8 不同模块在Set5数据集“butterfly”图像4倍超分辨率重构比较

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图 9 不同损失函数在Set5数据集“bird”图像4倍超分辨率重构比较

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表 1 不同方法各数据集的PSNR/dB和SSIM均值比较(×4)

算法	Set5		Set14		BSDS100		Manga109
算法	PSNR↑	SSIM↑	PSNR↑	SSIM↑	PSNR↑	SSIM↑	PSNR↑	SSIM↑
SRGAN^[11]	28.574	0.818	25.674	0.692	25.156	0.654	26.488	0.828
ESRGAN^[12]	30.438	0.852	26.278	0.699	25.323	0.651	28.245	0.859
SFTGAN^[14]	27.578	0.809	26.968	0.729	25.501	0.653	28.182	0.858
DSGAN^[17]	30.392	0.854	26.644	0.714	25.447	0.655	27.965	0.853
SRCGAN^[13]	28.068	0.789	26.071	0.696	25.659	0.657	25.295	0.796
FxSR^[15]	30.637	0.849	26.708	0.719	26.144	0.684	27.647	0.844
SROOE^[16]	30.862	0.866	27.231	0.731	26.195	0.687	27.852	0.849
WGSR^[19]	30.373	0.851	27.023	0.727	26.372	0.684	28.287	0.861
本文	30.904	0.872	27.715	0.749	26.838	0.701	28.312	0.867

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表 2 自制数据集不同方法NIQE和FVSD平均值比较(×4)

算法	NIQE↓	FVSD↑
SRGAN^[11]	12.84	3.84
ESRGAN^[12]	8.62	6.46
SFTGAN^[14]	8.46	6.35
DSGAN^[17]	8.41	6.51
SRCGAN^[13]	10.21	4.25
FxSR^[15]	8.37	6.48
SROOE^[16]	8.19	6.49
WGSR^[19]	8.14	6.52
本文	8.11	6.54

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表 3 不同滤波器重构效果的影响

滤波器	PSNR(dB)↑	SSIM↑
无	28.831	0.835
邻域平均	28.941	0.833
高斯差分	29.015	0.837

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表 4 含有不同模块对应的PSNR/dB和SSIM均值

分支结构	SFT层	质量评估网络	PSNR↑	SSIM↑
$\surd $	$ \times $	$ \times $	28.772	0.828
$ \times $	$\surd $	$ \times $	28.402	0.821
$ \times $	$ \times $	$\surd $	28.642	0.823
$\surd $	$\surd $	$\surd $	29.015	0.837

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表 5 不同损失函数的影响

损失组合	颜色损失		多层感知损失	对抗损失		FVSD损失	PSNR↑	SSIM↑
损失组合	Lcol	Lcol-1	多层感知损失	Ladv	Ladv-1	FVSD损失	PSNR↑	SSIM↑
组合1	$ \times $	$\surd $	$\surd $	$ \times $	$\surd $	$ \times $	28.352	0.818
组合2	$ \times $	$\surd $	$\surd $	$ \times $	$\surd $	$\surd $	28.831	0.835
组合3	$\surd $	$ \times $	$\surd $	$\surd $	$ \times $	$ \times $	28.437	0.821
本文	$\surd $	$ \times $	$\surd $	$\surd $	$ \times $	$\surd $	29.015	0.837

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表 6 重构时间与参数量的比较

算法	重构时间(msec)	参数量(MB)
SRGAN^[11]	0.0401	1.51
ESRGAN^[12]	0.1603	16.69
SFTGAN^[14]	0.0664	1.83
DSGAN^[17]	0.1723	16.69
SRCGAN^[13]	0.0096	0.38
FxSR^[15]	0.3541	18.30
SROOE^[16]	0.3880	70.20
WGSR^[19]	0.1806	16.69
本文	0.1568	9.62

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参考文献(28)

[1]	蔡文郁, 张美燕, 吴岩, 等. 基于循环生成对抗网络的超分辨率重建算法研究[J]. 电子与信息学报, 2022, 44(1): 178–186. doi: 10.11999/JEIT201046. CAI Wenyu, ZHANG Meiyan, WU Yan, et al. Research on cyclic generation countermeasure network based super-resolution image reconstruction algorithm[J]. Journal of Electronics & Information Technology, 2022, 44(1): 178–186. doi: 10.11999/JEIT201046.
[2]	ZHOU Chaowei and XIONG Aimin. Fast image super-resolution using particle swarm optimization-based convolutional neural networks[J]. Sensors, 2023, 23(4): 1923. doi: 10.3390/s23041923.
[3]	WU Zhijian, LIU Wenhui, LI Jun, et al. SFHN: Spatial-frequency domain hybrid network for image super-resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023, 33(11): 6459–6473. doi: 10.1109/TCSVT.2023.3271131.
[4]	程德强, 袁航, 钱建生, 等. 基于深层特征差异性网络的图像超分辨率算法[J]. 电子与信息学报, 2024, 46(3): 1033–1042. doi: 10.11999/JEIT230179. CHENG Deqiang, YUAN Hang, QIAN Jiansheng, et al. Image super-resolution algorithms based on deep feature differentiation network[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1033–1042. doi: 10.11999/JEIT230179.
[5]	SAHARIA C, HO J, CHAN W, et al. Image super-resolution via iterative refinement[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 4713–4726. doi: 10.1109/TPAMI.2022.3204461.
[6]	DONG Chao, LOY C C, HE Kaiming, et al. Learning a deep convolutional network for image super-resolution[C]. Proceedings of the 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 184–199. doi: 10.1007/978-3-319-10593-2_13.
[7]	KIM J, LEE J K, and LEE K M. Accurate image super-resolution using very deep convolutional networks[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016. doi: 10.1109/CVPR.2016.182.
[8]	TONG Tong, LI Gen, LIU Xiejie, et al. Image super-resolution using dense skip connections[C]. Proceedings of 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 4809–4817. doi: 10.1109/ICCV.2017.514.
[9]	LAN Rushi, SUN Long, LIU Zhenbing, et al. MADNet: A fast and lightweight network for single-image super resolution[J]. IEEE Transactions on Cybernetics, 2021, 51(3): 1443–1453. doi: 10.1109/TCYB.2020.2970104.
[10]	WEI Pengxu, XIE Ziwei, LU Hannan, et al. Component divide-and-conquer for real-world image super-resolution[C]. Proceedings of the 16th Europe Conference on Computer Vision, Glasgow, UK, 2020: 101–117. doi: 10.1007/978-3-030-58598-3_7.
[11]	LEDIG C, THEIS L, HUSZÁR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 105–114. doi: 10.1109/CVPR.2017.19.
[12]	WANG Xintao, YU Ke, WU Shixiang, et al. ESRGAN: Enhanced super-resolution generative adversarial networks[C]. Proceedings of the European Conference on Computer Vision, Munich, Germany, 2019: 63–79. doi: 10.1007/978-3-030-11021-5_5.
[13]	UMER R M, FORESTI G L, and MICHELONI C. Deep generative adversarial residual convolutional networks for real-world super-resolution[C]. Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, USA, 2020: 1769–1777. doi: 10.1109/CVPRW50498.2020.00227.
[14]	WANG Xintao, YU Ke, DONG Chao, et al. Recovering realistic texture in image super-resolution by deep spatial feature transform[C]. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 606–615. doi: 10.1109/CVPR.2018.00070.
[15]	PARK S H, MOON Y S, and CHO N I. Flexible style image super-resolution using conditional objective[J]. IEEE Access, 2022, 10: 9774–9792. doi: 10.1109/ACCESS.2022.3144406.
[16]	PARK S H, MOON Y S, and CHO N I. Perception-oriented single image super-resolution using optimal objective estimation[C]. Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 1725–1735. doi: 10.1109/CVPR52729.2023.00172.
[17]	FRITSCHE M, GU Shuhang, and TIMOFTE R. Frequency separation for real-world super-resolution[C]. Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop, Seoul, Korea (South), 2019: 3599–3608. doi: 10.1109/ICCVW.2019.00445.
[18]	PRAJAPATI K, CHUDASAMA V, PATEL H, et al. Direct unsupervised super-resolution using generative adversarial network (DUS-GAN) for real-world data[J]. IEEE Transactions on Image Processing, 2021, 30: 8251–8264. doi: 10.1109/TIP.2021.3113783.
[19]	KORKMAZ C, TEKALP A M, and DOGAN Z. Training generative image super-resolution models by wavelet-domain losses enables better control of artifacts[C]. Proceedings of 2014 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 5926–5936. doi: 10.1109/CVPR52733.2024.00566.
[20]	MA Chao, YANG C Y, YANG Xiaokang, et al. Learning a no-reference quality metric for single-image super-resolution[J]. Computer Vision and Image Understanding, 2017, 158: 1–16. doi: 10.1016/j.cviu.2016.12.009.
[21]	RONNEBERGER O, FISCHER P, and BROX T. U-Net: Convolutional networks for biomedical image segmentation[C]. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 2015: 234–241. doi: 10.1007/978-3-319-24574-4_28.
[22]	YANG Jianchao, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861–2873. doi: 10.1109/TIP.2010.2050625.
[23]	ZHANG Kai, ZUO Wangmeng, and ZHANG Lei. Deep plug-and-play super-resolution for arbitrary blur kernels[C]. Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019. doi: 10.1109/CVPR.2019.00177.
[24]	TIMOFTE R, AGUSTSSON E, VAN GOOL L, et al. NTIRE 2017 challenge on single image super-resolution: Methods and results[C]. Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, USA, 2017: 114–125. doi: 10.1109/CVPRW.2017.149.
[25]	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. doi: 10.5244/C.26.135. (查阅网上资料,未找到对应的出版地信息,请确认) .
[26]	ZEYDE R, ELAD M, and PROTTER M. On single image scale-up using sparse-representations[C]. Proceedings of the 7th International Conference on Curves and Surfaces, Avignon, France, 2012: 711–730. doi: 10.1007/978-3-642-27413-8_47.
[27]	ARBELÁEZ P, MAIRE M, FOWLKES C, et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898–916. doi: 10.1109/tpami.2010.161.
[28]	MATSUI Y, ITO K, ARAMAKI Y, et al. Sketch-based manga retrieval using manga109 dataset[J]. Multimedia Tools and Applications, 2017, 76(20): 21811–21838. doi: 10.1007/s11042-016-4020-z.