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The improved deep plug-and-play super-resolution with residual-in-residual dense block for arbitrary blur kernels

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Abstract

Single-image super-resolution (SISR) reconstruction has highly academic and practical values. The deep plug-and-play super-resolution (DPSR) framework has been proposed to super-resolve low-resolution (LR) images with arbitrary blur kernels. However, DPSR does not make full use of hierarchical features from original LR images, thereby achieving relatively-low performance, such as getting low average peak signal to noise ratio (PSNR) and structural similarity (SSIM) values. Considering residual-in-residual dense block (RRDB) can exploit hierarchical features, in this paper, firstly, RRDB is introduced to design an improved DPSR (IDPSR) framework with RRDB for arbitrary blur kernels. Secondly, the RRDB is adopted to replace the deep feature extraction part in DPSR in order to extract abundant local features, which makes the network capacity higher benefiting from the dense connections. The residual learning in different levels in RRDB can obtain high quality images. Finally, the test experiments are based on Set5, Set14, Urban100 and BSD100 datasets. The experimental results show that, under different blur kernels and different scale factors, PSNR and SSIM values of our proposed method increase by 0.34dB and 0.68%, respectively; under different noise levels, the average PSNR and SSIM values increase by 0.27dB and 1.01%, respectively.

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Data Availability

The toy tasks dataset analyzed during the current study are available publicly in the Set5 repository (http://people.rennes.inria.fr/Aline.Roumy/results/SR_BMVC12.html), Set14 repository (https://sites.google.com/site/romanzeyde/research-interests), BSD100 repository (https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/) and Urban100 repository (https://sites.google.com/site/jbhuang0604/publications/struct_sr).

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (51905416, 51804249), Xi’an Science and Technology Program (2022JH-RGZN-0041), Qin Chuangyuan Scientists + Engineers Team Construction Program in Shaanxi Province(2022KXJ-38), the Natural Science Basic Research Program of Shaanxi (Grant No. 2021JQ-574) and Scientific Research Plan Projects of Shaanxi Education Department20JK0758).

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Authors and Affiliations

  1. College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi Province, China

    Chao Xu

  2. College of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi Province, China

    Xiaoling Yang, Xiangdong Huang & Hongguang Pan

  3. CHN Energy Shendong Coal Group Design Company, Shenmu, 719315, Shaanxi Province, China

    Shan Li

  4. College of Artificial Intelligence, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi Province, China

    Xinyu Lei

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Correspondence to Hongguang Pan.

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Xu, C., Yang, X., Li, S. et al. The improved deep plug-and-play super-resolution with residual-in-residual dense block for arbitrary blur kernels. Pattern Anal Applic 26, 1657–1670 (2023). https://doi.org/10.1007/s10044-023-01192-6

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