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Compressive sensing spatially adaptive total variation method for high-noise astronomical image denoising

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Abstract

High-noise astronomical-image denoising has always been a research hotspot in deep space exploration. Compressive sensing (CS) is an advanced technology used for high-dimensional signal processing. It is useful for processing high-resolution astronomical images. To obtain high-quality astronomical images, a CS spatially adaptive total variation iterative (CSSATVI) method is proposed herein. In this method, a curvelet transform based on an adaptive curvelet soft thresholding operator is proposed to adaptively remove hidden noise information in the process of image sparse representation, and a novel CS denoising reconstruction model proposed is used to deeply mine the texture, edge and other detailed information. Moreover, a novel reconstruction strategy is proposed for preserving detailed image information in the iterative reconstruction process to obtain high-quality astronomical images. Simulation results indicated that the proposed CSSATVI method can quickly reconstruct a high-quality astronomical image and preserve a large amount of astronomical image details; thus, it can be effectively applied in deep space exploration.

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References

  1. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  2. Shen, H.F., Li, X.H., Zhang, L.P., Tao, D.S., et al.: Compressed sensing-based inpainting of aqua moderate resolution imaging spectroradiometer band 6 using adaptive spectrum-weighted sparse bayesian dictionary learning. IEEE Trans. Geosci. Remote Sens. 52, 894–906 (2014)

    Article  Google Scholar 

  3. Zhang, J., Zhang, H.L., Shi, X.P., Peng, X., et al.: Comppressed sensing for high-noise astronomical image. J. Electron. Imaging 28, 053026 (2019)

    Article  Google Scholar 

  4. Zha, Z.Y., Liu, X., Zhang, X.G., Chen, Y., et al.: Compressed sensing image reconstruction via adaptive sparse nonlocal regularization. Vis. Comput. 34, 117–137 (2018)

    Article  Google Scholar 

  5. Ito, D., Takabe, S.S., Wadayama, T.: Trainable ISTA for sparse signal recovery. IEEE Trans. Signal Process. 12, 3113–3125 (2019)

    Article  MathSciNet  Google Scholar 

  6. Ma, J.W., Dimet, F.L.: Deblurring from highly incomplete measurement for remote sensing. IEEE Trans. Geosci. Remote Sens. 47, 792–802 (2009)

  7. Yang, G.L., Li C.S., Guo, Y.D., et al.: The curvelet compressed sensing denoising algorithm for tobacco insect images. In: IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), pp. 387-391 (2021)

  8. Khmag, A., Ramli, A.R., AI-haddad, S.A.R., et al.: Denoising of natural images through robust wavelet thresholding and genetic programming. Vis. Comput. 33, 1141–1154 (2017)

    Article  Google Scholar 

  9. Thanh, D.N.H., Thanh, L.T., Hien, N.N., Prasath, S.: Adaptive total variation L1 regularization for salt and pepper image denoising. Optik 208, 163677 (2020)

    Article  Google Scholar 

  10. Ming, H.E.: Salt and pepper noise image denoising based on L1 norm and adaptive total variation. J. Southwest Norm. Univ. (Naturral Sci. Ed.) 05, 115–120 (2021)

    Google Scholar 

  11. Chen, H., Qin, Yl., Ren, H.L., Chang, L.P., et al.: Adaptive weighted high frequency iterative algorithm for fractional order total variation with nonlocal regularization for image reconstruction. Electronics 9, 1103 (2020)

    Article  Google Scholar 

  12. Kayalvizhi, S., Malarvizhi, S.: A novel encrypted compressive sensing of images based on fractional order hyper chaotic Chen system and DNA operations. Multimed. Tools Appl. 79, 3957–3974 (2020)

    Article  Google Scholar 

  13. Zhang, X.H., Lian, Q.S., Yang, Y.C., Su, Y.M.: A deep unrolling network inspired by total variation for compressed sensing MRI. Signal Process. 107, 102856 (2020)

    Google Scholar 

  14. Hanumanth, P., Bhavana, P., Subbarayappa, S.: Application of deep learning and compressed sensing for reconstruction of images. J. Phys. Conf. Ser. 1706, 012068 (2020)

    Article  Google Scholar 

  15. Yan, L.Q., Ma, Q.F., Chen, Y.J., Zhang, X.Y., et al.: Video captioning using global-local representation. IEEE Trans. Circuits Syst. Video Technol. 32, 6642–6656 (2022)

    Article  Google Scholar 

  16. Liu, D.F., Cui, Y.M., Tan, W.B., Chen, Y.J.: SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 9816-9825 (2021)

  17. Wang, Q.F., Yang, L., Quan, X.J., Feng, F.L.: Learning to generate question by asking question: a primal-dual approach with uncommon word generation. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), PP. 46-61 (2022)

  18. Wang, Q.F., Fang, Y., Ravula, A., He, R.N., et al.: Deep partial multiplex network embedding. In: Companion Proceedings of the Web Conference (WWW), pp.1053-1062 (2022)

  19. Yan, L.Q., Cui, Y.M., Chen, Y.J., Liu, D.F.: Hierarchical attention fusion for geo-localization. In: 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 6-11 (2021)

  20. Cui Y.M., Cao, Z.W., Xie, Y.X., Jiang, X.Y., et al.: DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 58-67 (2022)

  21. Ueda, T., Ohno, Y., Yamamoto, K., et al.: Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: Utility for improving image quality and examination time in routine clinical practice. Eur. J. Radiol. 134, 109430 (2021)

    Article  Google Scholar 

  22. Sun, Y., Chen, J., Liu, Q., Liu, B., et al.: Dual-path attention network for compressed sensing image reconstruction. IEEE Trans. Image Process. 29, 9482–9495 (2020)

  23. Zhao, D., Zhao, F., Gan, Y.: Reference-driven compressed sensing MR image reconstruction using deep convolutional neural networks without pre-Training. Sensors 20, 308 (2020)

  24. Kulkarni, A., Sreedevi, D.K.: Image denoising using wavelet based curvelet transform. Solid State Technol. 63, 4871–4876 (2020)

    Google Scholar 

  25. Reddy, P.L., Pawar, S.: Multispectral image denoising using curvelet transform and kriging interpolation based Winer filter. Des. Eng. 05, 838–849 (2021)

    Google Scholar 

  26. Temchenko, V.S., Gigolo, A.I., Kuznetsov, G.Y.: A new approach to antenna array calibration using compressive sensing. Proc. Radiat. Scatter. Electromagn. Waves 07, 163–166 (2021)

    Google Scholar 

  27. Xing, Y., Duan, Y., Indurkar, P.P., Qiu, A., et al.: Optical breast atlas as a testbed for image reconstruction in optical mammography. Sci. Data 257, 1–18 (2021)

    Google Scholar 

  28. Zhang, J., Zhang, H.L., Shi, X.P.: High noise astronomical image denoising via 2G-Bandelet denoising compressed sensing. Optik 184, 377–388 (2019)

    Article  Google Scholar 

  29. Carmona, R.A., Zhong, S.: Adaptive smoothing respecting feature directions. IEEE Trans. Image Process. 03, 353–358 (1998)

    Article  Google Scholar 

  30. Ruan, Y.D., Fang, H.Z., Chen, Q.M.: Semiblind image deconvolution with spatially adaptive total variation regulairzation. Math. Probl. Eng. 2014, 606170 (2014)

    Article  Google Scholar 

  31. Yan, L.X., Fang, H.Z., Sheng, Z.: Blind image deconvolution with spatially adaptive total variation regularization. Opt. Lett. 37, 2778–2780 (2012)

    Article  Google Scholar 

  32. Yalavarthy, P.K., Kalva, S.K., Parmanik, M., Prakash, J.: Non-local means improves total variation constrained photoacoustic image reconstruction. J. Biophoton. 14, e202000191 (2021)

    Article  Google Scholar 

  33. Yahya, A.A., Tan, J.Q., Su, B.Y., et al.: BM3D image denoising algorithm based on an adaptive filtering. Multimed. Tools Appl. 79, 20391–20427 (2020)

    Article  Google Scholar 

  34. Rejeesh, M.R., Thejaswini, P.M.O.T.F.: MOTF: multi-objective optimal Trilateral Filtering based partial moving frame algorithm for image denoising. Multimed. Tools Appl. 79(37–38), 28411–28430 (2020)

    Article  Google Scholar 

  35. Yang, H., Li, Q.: Research and application of seismic data denoising and reconstruction method based on compressed sensing. In: IOP Conference Series: Earth and Environmental Science. 671, 012042 (2021)

  36. Schlemper, J., Yang, G., Ferreira, P., et al.: Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI. In: International Conference on Medical Image Computing and Computer-Assisted Intervention(MICCAI). pp. 295-303 (2018)

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Acknowledgements

This work is supported by the grants from National Science Foundation of China (No.62102373, 61873246, 62006213); Henan Youth Talent Promotion Project (No.2022HYTP005).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Jie Zhang, Fengxian Wang & Huanlong Zhang

  2. School of Astronautics, Harbin Institute of Technology, Harbin, 150080, China

    Xiaoping Shi

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  1. Jie Zhang
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  2. Fengxian Wang
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  3. Huanlong Zhang
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  4. Xiaoping Shi
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Correspondence to Jie Zhang.

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Zhang, J., Wang, F., Zhang, H. et al. Compressive sensing spatially adaptive total variation method for high-noise astronomical image denoising. Vis Comput 40, 1215–1227 (2024). https://doi.org/10.1007/s00371-023-02842-w

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