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

HAN Yulan; CUI Yujie; LUO Yihong; LAN Chaofeng

doi:10.11999/JEIT240388

Volume 46 Issue 12

Dec. 2024

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HAN Yulan, CUI Yujie, LUO Yihong, LAN Chaofeng. Frequency Separation Generative Adversarial Super-resolution Reconstruction Network Based on Dense Residual and Quality Assessment[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4563-4574. doi: 10.11999/JEIT240388

Citation:

HAN Yulan, CUI Yujie, LUO Yihong, LAN Chaofeng. Frequency Separation Generative Adversarial Super-resolution Reconstruction Network Based on Dense Residual and Quality Assessment[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4563-4574. doi: 10.11999/JEIT240388

Citation:

HAN Yulan, CUI Yujie, LUO Yihong, LAN Chaofeng. Frequency Separation Generative Adversarial Super-resolution Reconstruction Network Based on Dense Residual and Quality Assessment[J]. Journal of Electronics & Information Technology, 2024, 46(12): 4563-4574. doi: 10.11999/JEIT240388

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

doi: 10.11999/JEIT240388 cstr: 32379.14.JEIT240388

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)

Received Date: 2024-05-16
Rev Recd Date: 2024-11-09

Available Online: 2024-11-18

Publish Date: 2024-12-01

Abstract

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 result, a frequency separation generative adversarial super-resolution reconstruction network based on dense residual 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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References(28)

References

[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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. The British Machine Vision Conference, 2012. doi: 10.5244/C.26.135.
[26]	ZEYDE R, ELAD M, and PROTTER M. On single image scale-up using sparse-representations[C]. 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.