Skip to main content
SpringerLink
Log in

HFAN: High-Frequency Attention Network for hyperspectral image denoising

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

During the last decades, learning-based deep neural network (DNN) has shown its advantages on hyperspectral image (HSI) denoising. Compared to classical prior-based methods, DNN-based algorithms employ a larger scale of training samples for learning to simulate the complex image generation process with higher accuracy. However, most DNN-based HSI denoising methods are designed by a superposition convolution layer, which cannot fully use the frequency information in the image itself, especially the information containing a strong response to noise in high-frequency domain. Thus, we propose a high-frequency attention network (HFAN) assisted by both spectral and spatial high-frequency information to achieve accurate HSIs denoising in this paper. Our proposed HFAN comprises a high-frequency and denoising branch, and the auxiliary function of high-frequency information is realized by transmitting the characteristic information of the high-frequency component to the denoising branch. Specifically, the spatial-spectral attention (SSA) module is presented to recover more detail in space and spectra. Experiments on synthetic and real HSI data show that our proposed HFAN achieves better denoising results compare to the other advanced methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availibility statement

The data that support the findings of this study are available from the corresponding author, C. W., upon reasonable request.

Notes

  1. http://lesun.weebly.com/hyperspectral-data-set.html.

  2. https://engineering.purdue.edu/biehl/MultiSpec/hyperspectral.html.

  3. http://www.tec.army.mil/hypercube.

References

  1. Aggarwal HK, Majumdar A (2015) Mixed gaussian and impulse denoising of hyperspectral images. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, pp 429–432

  2. Arad B, Ben-Shahar O (2016) Sparse recovery of hyperspectral signal from natural rgb images. In: European Conference on Computer Vision, Springer, pp 19–34

  3. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), IEEE, pp 60–65

  4. Cao X, Yao J, Xu Z et al (2020) Hyperspectral image classification with convolutional neural network and active learning. IEEE Trans Geosci Remote Sens 58(7):4604–4616

    Google Scholar 

  5. Cao X, Fu X, Xu C et al (2021) Deep spatial-spectral global reasoning network for hyperspectral image denoising. IEEE Trans Geosci Remote Sens 60:1–14

    Google Scholar 

  6. Chang Y, Yan L, Zhong S (2017) Hyper-laplacian regularized unidirectional low-rank tensor recovery for multispectral image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4260–4268

  7. Chang Y, Yan L, Fang H et al (2018) Hsi-denet: Hyperspectral image restoration via convolutional neural network. IEEE Trans Geosci Remote Sens 57(2):667–682

    Google Scholar 

  8. Chen Y, Cao X, Zhao Q et al (2017) Denoising hyperspectral image with non-iid noise structure. IEEE Trans Cybernet 48(3):1054–1066

    Google Scholar 

  9. Chen Y, Guo Y, Wang Y et al (2017) Denoising of hyperspectral images using nonconvex low rank matrix approximation. IEEE Trans Geosci Remote Sens 55(9):5366–5380

    Google Scholar 

  10. Chen Y, Huang TZ, Deng LJ et al (2017) Group sparsity based regularization model for remote sensing image stripe noise removal. Neurocomputing 267:95–106

    Google Scholar 

  11. Chen Y, Huang TZ, Zhao XL et al (2017) Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint. Remote Sens 9(6):559

    Google Scholar 

  12. Chen Y, He W, Yokoya N et al (2019) Hyperspectral image restoration using weighted group sparsity-regularized low-rank tensor decomposition. IEEE Trans Cybernet 50(8):3556–3570

    Google Scholar 

  13. Chen Y, Li J, Zhou Y (2020) Hyperspectral image denoising by total variation-regularized bilinear factorization. Signal Processing 174(107):645

    Google Scholar 

  14. Deng LJ, Huang TZ, Zhao XL et al (2018) A directional global sparse model for single image rain removal. Appl Math Modell 59:662–679

    MathSciNet  Google Scholar 

  15. Deng LJ, Vivone G, Jin C et al (2020) Detail injection-based deep convolutional neural networks for pansharpening. IEEE Trans Geosci Remote Sens 59(8):6995–7010

    Google Scholar 

  16. Dong W, Wang H, Wu F et al (2019) Deep spatial-spectral representation learning for hyperspectral image denoising. IEEE Trans Comput Imag 5(4):635–648

    Google Scholar 

  17. Dou HX, Huang TZ, Deng LJ et al (2018) Directional l0 sparse modeling for image stripe noise removal. Remote Sens 10(3):361

    Google Scholar 

  18. Fang L, Li S, Kang X et al (2015) Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model. IEEE Trans Geosci Remote Sens 53(8):4186–4201

    Google Scholar 

  19. Goetz AF (2009) Three decades of hyperspectral remote sensing of the earth: A personal view. Remote Sens Environ 113:S5–S16

    Google Scholar 

  20. He T, Zhang Z, Zhang H, et al. (2019a) Bag of tricks for image classification with convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 558–567

  21. He W, Zhang H, Zhang L et al (2015) Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration. IEEE Trans Geosci Remote Sens 54(1):178–188

    Google Scholar 

  22. He W, Yao Q, Li C, et al (2019b) Non-local meets global: An integrated paradigm for hyperspectral denoising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 6868–6877

  23. Hong D, Gao L, Yokoya N et al (2020) More diverse means better: Multimodal deep learning meets remote-sensing imagery classification. IEEE Trans Geosci Remote Sens 59(5):4340–4354

    Google Scholar 

  24. Ji S, Xu W, Yang M et al (2012) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal Machine Intell 35(1):221–231

    Google Scholar 

  25. Jin C, Deng LJ, Huang TZ et al (2022) Laplacian pyramid networks: A new approach for multispectral pansharpening. Information Fusion 78:158–170

    Google Scholar 

  26. Jin ZR, Zhang TJ, Jiang TX, et al (2022b) Lagconv: Local-context adaptive convolution kernels with global harmonic bias for pansharpening. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)

  27. Lewis SA, Hudak AT, Ottmar RD et al (2011) Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of alaska, usa. Int J Wildland Fire 20(2):255–271

    Google Scholar 

  28. Lillesand T, Kiefer RW, Chipman J (2015) Remote sensing and image interpretation. John Wiley & Sons, USA

    Google Scholar 

  29. Liu W, Lee J (2019) A 3-d atrous convolution neural network for hyperspectral image denoising. IEEE Trans Geosci Remote Sens 57(8):5701–5715

    Google Scholar 

  30. Lu T, Li S, Fang L et al (2015) Spectral-spatial adaptive sparse representation for hyperspectral image denoising. IEEE Trans Geosci Remote Sens 54(1):373–385

    Google Scholar 

  31. Lu X, Wang Y, Yuan Y (2013) Graph-regularized low-rank representation for destriping of hyperspectral images. IEEE Trans Geosci Remote Sens 51(7):4009–4018

    Google Scholar 

  32. Luo YS, Zhao XL, Jiang TX et al (2021) Hyperspectral mixed noise removal via spatial-spectral constrained unsupervised deep image prior. IEEE J Sel Top Appl Earth Obs Remote Sens 14:9435–9449

    Google Scholar 

  33. Maggioni M, Katkovnik V, Egiazarian K et al (2012) Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans Image Process 22(1):119–133

    MathSciNet  PubMed  Google Scholar 

  34. Marion F. Baumgardner LLB, Landgrebe DA (2015) 220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3. https://doi.org/10.4231/R7RX991C, https://purr.purdue.edu/publications/1947/1

  35. Pande-Chhetri R, Abd-Elrahman A (2011) De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering. ISPRS J Photogramm Remote Sens 66(5):620–636

    Google Scholar 

  36. Pang L, Gu W, Cao X (2022) Trq3dnet: A 3d quasi-recurrent and transformer based network for hyperspectral image denoising. Remote Sens 14(18):4598

    Google Scholar 

  37. Peng Y, Meng D, Xu Z, et al (2014) Decomposable nonlocal tensor dictionary learning for multispectral image denoising. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2949–2956

  38. Plaza A, Benediktsson JA, Boardman JW et al (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122

    Google Scholar 

  39. Qian Y, Ye M (2012) Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation. IEEE J Sel Top Appl Earth Obs Remote Sens 6(2):499–515

    MathSciNet  Google Scholar 

  40. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241

  41. Shi Q, Tang X, Yang T et al (2021) Hyperspectral image denoising using a 3-d attention denoising network. IEEE Trans Geosci Remote Sens 59(12):10,348-10,363

    Google Scholar 

  42. Su H, Du Q, Chen G et al (2014) Optimized hyperspectral band selection using particle swarm optimization. IEEE J Sel Top Appl Earth Obs Remote Sens 7(6):2659–2670

    Google Scholar 

  43. Tiwari K, Arora MK, Singh D (2011) An assessment of independent component analysis for detection of military targets from hyperspectral images. Int J Appl Earth Obs Geoinf 13(5):730–740

    Google Scholar 

  44. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  45. Wang C, Shen HZ, Fan F, et al (2021a) Eaa-net: A novel edge assisted attention network for single image dehazing. Knowledge-Based Systems p. 107279

  46. Wang Y, Deng LJ, Zhang TJ, et al. (2021b) Ssconv: Explicit spectral-to-spatial convolution for pansharpening. In: Proceedings of the 29th ACM International Conference on Multimedia, pp 4472–4480

  47. Wang Z, Bovik AC, Sheikh HR et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    PubMed  Google Scholar 

  48. Wei K, Fu Y, Huang H (2020) 3-d quasi-recurrent neural network for hyperspectral image denoising. IEEE Trans Neural Netw Learn Syst 32(1):363–375

    Google Scholar 

  49. Wu X, Huang TZ, Deng LJ, et al (2021) Dynamic cross feature fusion for remote sensing pansharpening. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 14,687–14,696

  50. Xiao JL, Huang TZ, Deng LJ, et al (2022) A new context-aware details injection fidelity with adaptive coefficients estimation for variational pansharpening. IEEE Trans Geosci Remote Sens

  51. Xie Q, Zhao Q, Meng D et al (2017) Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery. IEEE Trans Pattern Anal Mach Intell 40(8):1888–1902

    PubMed  Google Scholar 

  52. Xue J, Zhao Y, Liao W et al (2017) Joint spatial and spectral low-rank regularization for hyperspectral image denoising. IEEE Trans Geosci Remote Sens 56(4):1940–1958

    Google Scholar 

  53. Yuan Q, Zhang L, Shen H (2012) Hyperspectral image denoising employing a spectral-spatial adaptive total variation model. IEEE Trans Geosci Remote Sens 50(10):3660–3677

    Google Scholar 

  54. Yuan Q, Zhang Q, Li J et al (2018) Hyperspectral image denoising employing a spatial-spectral deep residual convolutional neural network. IEEE Trans Geosci Remote Sens 57(2):1205–1218

    Google Scholar 

  55. Zhang H, He W, Zhang L et al (2013) Hyperspectral image restoration using low-rank matrix recovery. IEEE Trans Geosci Remote Sens 52(8):4729–4743

    Google Scholar 

  56. Zhang J, Cai Z, Chen F et al (2022) Hyperspectral image denoising via adversarial learning. Remote Sens 14(8):1790

    Google Scholar 

  57. Zhang L, Zhang L, Tao D et al (2013) Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Trans Geosci Remote Sens 52(8):4955–4965

    Google Scholar 

  58. Zhang Q, Yuan Q, Li J et al (2019) Hybrid noise removal in hyperspectral imagery with a spatial-spectral gradient network. IEEE Trans Geosci Remote Sens 57(10):7317–7329

    Google Scholar 

  59. Zhang Q, Yuan Q, Li J et al (2020) Deep spatio-spectral bayesian posterior for hyperspectral image non-iid noise removal. ISPRS J Photogramm Remote Sens 164:125–137

    Google Scholar 

  60. Zhang T, Fu Y, Li C (2021) Hyperspectral image denoising with realistic data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 2248–2257

  61. Zhang TJ, Deng LJ, Huang TZ, et al (2022b) A triple-double convolutional neural network for panchromatic sharpening. IEEE Trans Neural Netw Learn Syst

  62. Zhao YQ, Yang J (2014) Hyperspectral image denoising via sparse representation and low-rank constraint. IEEE Trans Geosci Remote Sens 53(1):296–308

    Google Scholar 

  63. Zhao YQ, Gong P, Pan Q (2008) Object detection by spectropolarimeteric imagery fusion. IEEE Trans Geosci Remote Sens 46(10):3337–3345

    Google Scholar 

  64. Zhou Y, Peng J, Chen CP (2014) Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification. IEEE Trans Geosci Remote Sens 53(2):1082–1095

    Google Scholar 

  65. Zhuang JH, Luo Y, Zhao XL et al (2021) Reconciling hand-crafted and self-supervised deep priors for video directional rain streaks removal. IEEE Signal Process Lett 28:2147–2151

    Google Scholar 

Download references

Acknowledgements

This work is supported by the Basic Public Welfare Research Program of Zhejiang Province (LGG22F020036), Natural Science Research Project of Anhui Universities (KJ2019A0032), Natural Science Foundation of Anhui Province (2008085QF286), National key research and development program of China (2021YFF0700203).

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhejiang Ocean University, Zhoushan, 316022, China

    Chuansheng Yang, Chao Zhang, Haozhen Shen, Tong Peng, Chao Wang & Hongming Chen

  2. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, China

    Chuansheng Yang, Tong Peng, Chao Wang & Hongming Chen

  3. School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Liangjian Deng

  4. Institute of Intelligent Materials and Sensing Engineering, Shenzhen Research Institute of Nankai University, Shenzhen, 518083, China

    Hongming Chen

  5. School of Mathematical Sciences, Anhui University, Hefei, 230601, China

    Liangtian He

Authors
  1. Chuansheng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haozhen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Liangjian Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hongming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Liangtian He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, C., Zhang, C., Shen, H. et al. HFAN: High-Frequency Attention Network for hyperspectral image denoising. Int. J. Mach. Learn. & Cyber. 15, 837–851 (2024). https://doi.org/10.1007/s13042-023-01942-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-023-01942-2

Keywords

Search

Navigation