Research on Blind Super-resolution Reconstruction with Double Discriminator

LU Di; YU Guoliang

doi:10.11999/JEIT221502

Volume 46 Issue 1

Jan. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 277-286

LU Di, YU Guoliang. Research on Blind Super-resolution Reconstruction with Double Discriminator[J]. Journal of Electronics & Information Technology, 2024, 46(1): 277-286. doi: 10.11999/JEIT221502

Citation:

LU Di, YU Guoliang. Research on Blind Super-resolution Reconstruction with Double Discriminator[J]. Journal of Electronics & Information Technology, 2024, 46(1): 277-286. doi: 10.11999/JEIT221502

LU Di, YU Guoliang. Research on Blind Super-resolution Reconstruction with Double Discriminator[J]. Journal of Electronics & Information Technology, 2024, 46(1): 277-286. doi: 10.11999/JEIT221502

Citation:

LU Di, YU Guoliang. Research on Blind Super-resolution Reconstruction with Double Discriminator[J]. Journal of Electronics & Information Technology, 2024, 46(1): 277-286. doi: 10.11999/JEIT221502

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Research on Blind Super-resolution Reconstruction with Double Discriminator

doi: 10.11999/JEIT221502

LU Di^,,
YU Guoliang

Harbin University of Science and Technology, Harbin 150080, China

Received Date: 2022-12-02
Rev Recd Date: 2023-09-13

Available Online: 2023-09-15

Publish Date: 2024-01-17

Abstract

Abstract

Image super-resolution reconstruction methods have very important uses in public safety detection, satellite imaging, medicine and photo restoration. In this paper, super-resolution reconstruction methods based on generative adversarial networks are investigated, from the training Real-world blind Enhanced Super-Resolution Generative Adversarial Networks pure synthetic data (Real-ESRGAN) method, a double UNet3+ discriminators Real-ESRGAN (DU3-Real-ESRGAN) method is proposed. Firstly, a UNet3+ structure is introduced in the discriminator to capture fine-grained details and coarse-grained semantics from the full scale. Secondly, a dual discriminator structure is adopted, with one discriminator learning image texture details and the other focusing on image edges to achieve complementary image information. Compared with several methods based on generative adversarial networks on Set5, Set14, BSD100 and Urban100 data sets, except for Set5, the Peak Signal to Noise Ration (PSNR), Structure SIMilarity (SSIM) and Natural Image Quality Evaluator (NIQE) values of the DU3-Real-ESRGAN method are superior to those of other methods to produce more intuitive and realistic high-resolution images.

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References(20)

References

[1]	陶状, 廖晓东, 沈江红. 双路径反馈网络的图像超分辨重建算法[J]. 计算机系统应用, 2020, 29(4): 181–186. doi: 10.15888/j.cnki.csa.007344 TAO Zhuang, LIAO Xiaodong, and SHEN Jianghong. Dual stream feedback network for image super-resolution reconstruction[J]. Computer Systems &Applications, 2020, 29(4): 181–186. doi: 10.15888/j.cnki.csa.007344
[2]	陈栋. 单幅图像超分辨率重建算法研究[D]. [硕士论文], 华南理工大学, 2020. CHEN Dong. Research on single image super-resolution reconstruction algorithm[D]. [Master dissertation], South China University of Technology, 2020.
[3]	KAPPELER A, YOO S, DAI Qiqin, et al. Video super-resolution with convolutional neural networks[J]. IEEE Transactions on Computational Imaging, 2016, 2(2): 109–122. doi: 10.1109/TCI.2016.2532323
[4]	JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 2017–2025.
[5]	IRANI M and PELEG S. Super resolution from image sequences[C]. [1990] Proceedings. 10th International Conference on Pattern Recognition, Atlantic City, USA, 1990: 115–120.
[6]	STARK H and OSKOUI P. High-resolution image recovery from image-plane arrays, using convex projections[J]. Journal of the Optical Society of America A, 1989, 6(11): 1715–1726. doi: 10.1364/JOSAA.6.001715
[7]	DONG Chao, LOY C C, HE Kaiming, et al. Learning a deep convolutional network for image super-resolution[C]. 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 184–199.
[8]	DONG Chao, LOY C C, and TANG Xiaoou. Accelerating the super-resolution convolutional neural network[C]. 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 391–407.
[9]	PARK S J, SON H, CHO S, et al. SRFeat: Single image super-resolution with feature discrimination[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 455–471.
[10]	ZHANG Yulun, LI Kunpeng, LI Kai, et al. Image super-resolution using very deep residual channel attention networks[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 294–310.
[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.
[12]	LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, USA, 2017: 1132–1140.
[13]	WANG Xintao, YU Ke, WU Shixiang, et al. ESRGAN: Enhanced super-resolution generative adversarial networks[C]. European Conference on Computer Vision, Munich, Germany, 2018: 63–79.
[14]	SOH J W, PARK G Y, JO J, et al. Natural and realistic single image super-resolution with explicit natural manifold discrimination[C]. The 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 8114-8123.
[15]	WANG Xintao, XIE Liangbin, DONG Chao, et al. Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data[C]. 2021 IEEE/CVF International Conference on Computer Vision Workshops, Montreal, Canada, 2021: 1905–1914.
[16]	SAJJADI M S M, SCHÖLKOPF B, and HIRSCH M. EnhanceNet: Single image super-resolution through automated texture synthesis[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 4501–4510.
[17]	ZHANG Kai, LI Yawei, ZUO Wangmeng, et al. Plug-and-play image restoration with deep denoiser prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10): 6360–6376. doi: 10.1109/TPAMI.2021.3088914
[18]	HUANG Huimin, LIN Lanfen, TONG Ruofeng, et al. UNet 3+: A full-scale connected UNet for medical image segmentation[C]. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020: 1055–1059.
[19]	ZHOU Zongwei, SIDDIQUEE M M R, TAJBAKHSH N, et al. Unet++: A nested U-Net architecture for medical image segmentation[M]. Stoyanov D, Taylor Z, Carneiro G, et al. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Cham: Springer, 2018: 3–11.
[20]	MITTAL A, SOUNDARARAJAN R, and BOVIK A C. Making a “Completely Blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209–212. doi: 10.1109/LSP.2012.2227726