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A super-resolution algorithm for tire images based on second-order channel attention

Published: 14 June 2024 Publication History

Abstract

Tire images are a type of crime scene investigation image that is useful in case detection. However, due to restrictions on the acquisition conditions, these images have a low resolution. Image super-resolution may be able to solve this problem. The super-resolution method, which uses the transformer structure as its backbone, has provided the best reconstruction results to date. Although these algorithms can model global features, they disregard correlations between channel features and do not prioritize the channels that are conducive to reconstruction. For textured tire images that contain textural characteristics, a second-order channel attention image super-resolution algorithm is proposed. It builds a parallel transformer layer to quantify the connection between channel features using second-order covariance statistics, such that the channels that contribute considerably to the results get more attention. Furthermore, it presents a non-linear reconstruction module, in which the dropout layer is connected after the upper-sampling layer, which can not only recover finer image details but also increase the network's stability and robustness. The results of the tire dataset and standard test dataset experiments indicate that our approach may give a more realistic and finely detailed image. Its PSNR value is greater than 0.06-0.12 dB, allowing for high-quality image reconstruction.

References

[1]
Greenspan, H. 2009. Super-resolution in medical imaging. The computer journal, 52(1), 43-63.
[2]
Isaac, J. S., & Kulkarni, R. 2015. Super resolution techniques for medical image processing. In 2015 International Conference on Technologies for Sustainable Development (ICTSD) (pp. 1-6). IEEE.
[3]
Huang, Y., Shao, L., & Frangi, A. F. 2017. Simultaneous super-resolution and cross-modality synthesis of 3D medical images using weakly-supervised joint convolutional sparse coding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6070-6079).
[4]
Zhang, L., Zhang, H., Shen, H., & Li, P. 2010. A super-resolution reconstruction algorithm for surveillance images. Signal Processing, 90(3), 848-859.
[5]
Rasti, P., Uiboupin, T., Escalera, S., & Anbarjafari, G. 2016. Convolutional neural network super resolution for face recognition in surveillance monitoring. In Articulated Motion and Deformable Objects: 9th International Conference, AMDO 2016, Palma de Mallorca, Spain, July 13-15, 2016, Proceedings 9 (pp. 175-184). Springer International Publishing.
[6]
Yi, P., Wang, Z., Jiang, K., Shao, Z., & Ma, J. 2019. Multi-temporal ultra dense memory network for video super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 30(8), 2503-2516.
[7]
Zhang, G., Wei, S., Pang, H., Qiu, S., & Zhao, Y. 2022. Composed Image Retrieval via Explicit Erasure and Replenishment With Semantic Alignment. IEEE Transactions on Image Processing, 31, 5976-5988.
[8]
Sajjadi, M. S., Scholkopf, B., & Hirsch, M. 2017. Enhancenet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE international conference on computer vision (pp. 4491-4500).
[9]
Haris, M., Shakhnarovich, G., & Ukita, N. 2021. Task-driven super resolution: Object detection in low-resolution images. In Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part V 28 (pp. 387-395). Springer International Publishing.
[10]
Dong, C., Loy, C. C., & Tang, X. 2016. Accelerating the super-resolution convolutional neural network. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14 (pp. 391-407). Springer International Publishing.
[11]
Shi, W., Caballero, J., Huszár, F., Totz, J. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1874-1883).
[12]
Kim, J., Lee, J. K., & Lee, K. M. 2016. Deeply-recursive convolutional network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1637-1645).
[13]
Mao, X. J., Shen, C., & Yang, Y. B. 2016. Image restoration using convolutional auto-encoders with symmetric skip connections.arXiv preprint arXiv:1606.08921.
[14]
Tong, T., Li, G., Liu, X., & Gao, Q. 2017. Image super-resolution using dense skip connections. In Proceedings of the IEEE international conference on computer vision(pp. 4799-4807).
[15]
Hui, Z., Gao, X., Yang, Y., & Wang, X. 2019, October. Lightweight image super-resolution with information multi-distillation network. In Proceedings of the 27th acm international conference on multimedia (pp. 2024-2032).
[16]
Muqeet, A., Hwang, J., Yang, S. 2020. Multi-attention based ultra lightweight image super-resolution. In Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16(pp. 103-118). Springer International Publishing.
[17]
Liu, J., Tang, J., & Wu, G. 2020. Residual feature distillation network for lightweight image super-resolution. In Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16(pp. 41-55). Springer International Publishing.
[18]
Vaswani, A., Shazeer, N., Parmar, N., 2017 Attention is all you need.Advances in neural information processing systems,30.
[19]
Touvron, H., Cord, M., Douze, M., 2021, July. Training data-efficient image transformers & distillation through attention. In International conference on machine learning(pp. 10347-10357). PMLR.
[20]
Dosovitskiy, A., Beyer, L., Kolesnikov, A., 2020. An image is worth 16x16 words: Transformers for image recognition at scale.arXiv preprint arXiv:2010.11929.
[21]
Carion, N., Massa, F., Synnaeve, G, 2020. End-to-end object detection with transformers. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16 (pp. 213-229). Springer International Publishing.
[22]
Chen, H., Wang, Y., Guo, T., 2021. Pre-trained image processing transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(pp. 12299-12310).
[23]
Cao, J., Li, Y., Zhang, K., & Van Gool, L. 2021. Video super-resolution transformer. arXiv preprint arXiv:2106.06847.
[24]
Liu, Z., Lin, Y., Cao, Y., 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 10012-10022).
[25]
Liang, J., Cao, J., Sun, G., 2021. Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1833-1844).
[26]
Dong, C., Loy, C. C., He, K., & Tang, X. 2015. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence, 38(2), 295-307.
[27]
Kim, J., Lee, J. K., & Lee, K. M. 2016. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1646-1654).
[28]
Lim, B., Son, S., Kim, H., Nah, S., & Mu Lee, K. 2017. Enhanced deep residual networks for single image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 136-144).
[29]
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., & Fu, Y. 2018. Image super-resolution using very deep residual channel attention networks. In Proceedings of the European conference on computer vision (ECCV) (pp. 286-301).
[30]
Yu, J., Wang, Z., Vasudevan, V., 2022. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917.
[31]
Wu, B., Xu, C., Dai, X., 2020. Visual transformers: Token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677.
[32]
Li, Y., Zhang, K., Cao, J., Timofte, R., & Van Gool, L. 2021. Localvit: Bringing locality to vision transformers. arXiv preprint arXiv:2104.05707.
[33]
Liu, Y., Sun, G., Qiu, Y., 2021. Transformer in convolutional neural networks. arXiv preprint arXiv:2106.03180, 3.
[34]
Liu, L., Ouyang, W., Wang, X., 2020. Deep learning for generic object detection: A survey. International journal of computer vision, 128, 261-318.
[35]
Wu, B., Xu, C., Dai, X., 2020. Visual transformers: Token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677.
[36]
Cao, H., Wang, Y., Chen, J., 2023. Swin-unet: Unet-like pure transformer for medical image segmentation. In Computer Vision–ECCV 2022 Workshops: Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part III (pp. 205-218). Cham: Springer Nature Switzerland.
[37]
Liang, D., Chen, X., Xu, W., Zhou, Y., & Bai, X. 2022. Transcrowd: weakly-supervised crowd counting with transformers. Science China Information Sciences, 65(6), 160104.
[38]
Sun, G., Liu, Y., Probst, T., 2021. Boosting crowd counting with transformers. arXiv preprint arXiv:2105.10926.
[39]
Chen, H., Wang, Y., Guo, T., 2021. Pre-trained image processing transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(pp. 12299-12310).
[40]
Wang, Z., Cun, X., Bao, J., 2022, . A general u-shaped transformer for image restoration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA(pp. 19-24).
[41]
Chen, H., Wang, Y., Guo, T., 2021. Pre-trained image processing transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(pp. 12299-12310).
[42]
Liang, J., Cao, J., Fan, Y., 2022. Vrt: A video restoration transformer. arXiv preprint arXiv:2201.12288.
[43]
Li, W., Lu, X., Lu, J., Zhang, X., & Jia, J. 2021. On efficient transformer and image pre-training for low-level vision. arXiv preprint arXiv:2112.10175.
[44]
Li, P., Xie, J., Wang, Q., & Zuo, W. 2017. Is second-order information helpful for large-scale visual recognition?. In Proceedings of the IEEE international conference on computer vision(pp. 2070-2078).
[45]
Liu, R., Su, Z., Lin, G., & Zhou, F. 2020. Second-order attention network for magnification-arbitrary single image super-resolution. In 2020 8th International Conference on Digital Home (ICDH)(pp. 127-134). IEEE.
[46]
Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., & Zeng, T. 2022. Transformer for single image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(pp. 457-466).
[47]
Kong, X., Liu, X., Gu, J., Qiao, Y., & Dong, C. 2022. Reflash dropout in image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(pp. 6002-6012).
[48]
CIIP-TPID (Center for Image and Information ProcessingTread Pattern Image Datasets) [EB/OL]. http: //www.xuptciip.com.cn/show.html?database-lthhhw, Xi'an University of Posts and Telecommunications, 2019.

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cover image ACM Other conferences
AIPR '23: Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
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Published: 14 June 2024

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Author Tags

  1. Image super-resolution
  2. Second-order channel attention
  3. Tire image
  4. Transformer

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Shaanxi Industrial Development Key Project
  • the Natural Science Basic Research Plan in Shaanxi Province of China
  • The National Natural Science Foundation of China

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AIPR 2023

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