Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism

NAVIGATE

Table of Contents

STATISTICS

Viewed452

Downloads373

Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism

[HTML] PDF下载 (373)

[1]BO Yangyu,WU Yongliang,WANG Xuejun.Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism[J].Journal of Zhengzhou University (Engineering Science),2024,45(06):48-55.[doi:10.13705/j.issn.1671-6833.2024.03.009]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 06 Page number: 48-55 Column: Public date: 2024-09-25

Title:: Image Super-resolution Reconstruction Network Based on Double FeatureExtraction and Attention Mechanism

Author(s):: BO Yangyu; WU Yongliang; WANG Xuejun; College of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Keywords:: image super-resolution reconstruction; local spatial attention; residual fusion attention; atrous pyramid; double feature extraction

CLC:: TP751 TP391. 41 TP183

DOI:: 10.13705/j.issn.1671-6833.2024.03.009

Abstract:: In the process of image super-resolution reconstruction, high frequency features might be ignored, whichwould lead to insufficient extraction features and fuzzy texture details in the reconstructed image. To solve this problem, an image super-resolution reconstruction network based on double feature extraction and attention mechanismwas proposed. In particular, in this study, a two-branch network for feature extraction was proposed to solve theproblem that high frequency features and multi-scale features could not be effectively extracted and uniformly fusedduring image reconstruction. In addition, in order to make the network obtain more accurate high-frequency features, a local spatial attention module was proposed, and combined with channel attention. A residual fusion attention module was constructed to improve the network′s ability to locate high-frequency features. Finally, the atrouspyramid module was designed to enlarge the receptive field of the network and enable the multi-scale feature extraction. Experiments were carried out on four benchmark datasets, and the results were better than the current advanced methods. Especially when the super-resolution multiple was 4, the proposed method improved the optimalPSNR by 0. 16, 0. 08, 0. 03 and 0. 20 dB, respectively, compared with the current mainstream models. The experimental results shown that the proposed method achieved better improvement in visual effect and quantitative analysis

References:: [1] 成科扬, 荣兰, 蒋森林, 等. 基于深度学习的遥感图像超分辨率重建方法综述[ J] . 郑州大学学报( 工学版) , 2022, 43(5) : 8-16.
CHENG K Y, RONG L, JIANG S L, et al. Overview ofmethods for remote sensing image super-resolution reconstruction based on deep learning[J]. Journal of ZhengzhouUniversity (Engineering Science), 2022, 43(5): 8-16.
[2] DONG C, LOY C C, HE K M, et al. Learning a deepconvolutional network for image super-resolution[C]∥European Conference on Computer Vision. Cham: Springer,2014: 184-199.
[3] DONG C, LOY C C, TANG X O. Accelerating the super-resolution convolutional neural network[ C]∥European Conference on Computer Vision. Cham: Springer,2016: 391-407.
[4] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[ C]∥2016IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE, 2016: 1646-1654.
[5] LIM B, SON S, KIM H, et al. Enhanced deep residualnetworks for single image super-resolution [ C ] ∥2017IEEE Conference on Computer Vision and Pattern Recognition Workshops ( CVPRW) . Piscataway:IEEE, 2017:1132-1140.
[6] LAI W S, HUANG J B, AHUJA N, et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C]∥2017 IEEE Conference on Computer Vision andPattern Recognition (CVPR) . Piscataway:IEEE, 2017:5835-5843.
[7] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C]∥2016 IEEEConference on Computer Vision and Pattern Recognition(CVPR) . Piscataway: IEEE, 2016: 1637-1645.
[8] TAI Y, YANG J, LIU X M. Image super-resolution viadeep recursive residual network[ C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR) . Piscataway:IEEE, 2017: 2790-2798.
[9] ZHANG Y L, LI K P, LI K, et al. Image super-resolution using very deep residual channel attention networks[C]∥European Conference on Computer Vision. Cham:Springer, 2018: 294-310.
[10] AHN N, KANG B, SOHN K A. Fast, accurate, andlightweight super-resolution with cascading residual network[C]∥Computer Vision—ECCV 2018: 15th European Conference. New York:ACM, 2018: 256-272.
[11] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]∥2018 IEEE / CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018:7132-7141.
[12] WU B C, WAN A, YUE X Y, et al. Shift: a zero FLOP,zero parameter alternative to spatial convolutions[C]∥2018IEEE / CVF Conference on Computer Vision and PatternRecognition. Piscataway: IEEE, 2018: 9127-9135.
[13] TONG T, LI G, LIU X J, et al. Image super-resolutionusing dense skip connections [ C] ∥2017 IEEE International Conference on Computer Vision ( ICCV) . Piscataway:IEEE, 2017: 4809-4817.
[14] YUAN Y, LIU S Y, ZHANG J W, et al. Unsupervisedimage super-resolution using cycle-in-cycle generative adversarial networks [ C]∥2018 IEEE / CVF Conference onComputer Vision and Pattern Recognition Workshops(CVPRW) . Piscataway:IEEE, 2018: 814-822.
[15] WANG Z, BOVIK A C, SHEIKH H R, et al. Imagequality assessment: from error visibility to structural similarity [ J ] . IEEE Transactions on Image Processing: APublication of the IEEE Signal Processing Society, 2004,13(4) : 600-612.
[16] YU F, KOLTUN V. Multi-scale context aggregation bydilated convolutions[EB / OL] . ( 2015 - 11 - 23) [ 2023 -06-14] . https:∥arxiv. org / abs/ 1511. 07122.
[17] LI W B, ZHOU K, QI L, et al. LAPAR: linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond [ EB / OL ] . ( 2021 - 05 - 21 )[2023-06-14] . https:∥arxiv. org / abs/ 2105. 10422.
[18] TAI Y, YANG J, LIU X M, et al. MemNet: a persistentmemory network for image restoration[C]∥2017 IEEE International Conference on Computer Vision ( ICCV) . Piscataway:IEEE, 2017: 4549-4557.
[19] WANG C F, LI Z, SHI J. Lightweight image super-resolution with adaptive weighted learning network[EB / OL] .(2019- 04 - 04 ) [ 2023 - 06 - 14 ] . https:∥arxiv. org /abs/ 1904. 02358.
[20] HUI Z, WANG X M, GAO X B. Fast and accurate singleimage super-resolution via information distillation network[C]∥2018 IEEE / CVF Conference on Computer Vision andPattern Recognition. Piscataway:IEEE, 2018: 723-731.
[21] HUI Z, GAO X B, YANG Y C, et al. Lightweight imagesuper-resolution with information multi-distillation network[C]∥Proceedings of the 27th ACM International Conferenceon Multimedia. New York: ACM, 2019: 2024-2032.
[22] ZHU F Y, ZHAO Q J. Efficient single image super-resolution via hybrid residual feature learning with compactback-projection network [ C] ∥2019 IEEE / CVF International Conference on Computer Vision Workshop ( ICCVW) . Piscataway:IEEE, 2019: 2453-2460.
[23] LIU J, TANG J, WU G S. Residual feature distillationnetwork for lightweight image super-resolution[C]∥European Conference on Computer Vision. Cham: Springer,2020: 41-55.
[24] KONG F Y, LI M X, LIU S W, et al. Residual local feature network for efficient super-resolution [ C ] ∥ 2022IEEE / CVF Conference on Computer Vision and PatternRecognition Workshops ( CVPRW) . Piscataway: IEEE,2022: 765-775.

Similar References:

Memo

Last Update: 2024-09-29