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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Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)
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)
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)
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)
Ito, D., Takabe, S.S., Wadayama, T.: Trainable ISTA for sparse signal recovery. IEEE Trans. Signal Process. 12, 3113–3125 (2019)
Ma, J.W., Dimet, F.L.: Deblurring from highly incomplete measurement for remote sensing. IEEE Trans. Geosci. Remote Sens. 47, 792–802 (2009)
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)
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)
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)
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)
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)
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)
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)
Hanumanth, P., Bhavana, P., Subbarayappa, S.: Application of deep learning and compressed sensing for reconstruction of images. J. Phys. Conf. Ser. 1706, 012068 (2020)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Kulkarni, A., Sreedevi, D.K.: Image denoising using wavelet based curvelet transform. Solid State Technol. 63, 4871–4876 (2020)
Reddy, P.L., Pawar, S.: Multispectral image denoising using curvelet transform and kriging interpolation based Winer filter. Des. Eng. 05, 838–849 (2021)
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)
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)
Zhang, J., Zhang, H.L., Shi, X.P.: High noise astronomical image denoising via 2G-Bandelet denoising compressed sensing. Optik 184, 377–388 (2019)
Carmona, R.A., Zhong, S.: Adaptive smoothing respecting feature directions. IEEE Trans. Image Process. 03, 353–358 (1998)
Ruan, Y.D., Fang, H.Z., Chen, Q.M.: Semiblind image deconvolution with spatially adaptive total variation regulairzation. Math. Probl. Eng. 2014, 606170 (2014)
Yan, L.X., Fang, H.Z., Sheng, Z.: Blind image deconvolution with spatially adaptive total variation regularization. Opt. Lett. 37, 2778–2780 (2012)
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)
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)
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)
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)
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)
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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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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DOI: https://doi.org/10.1007/s00371-023-02842-w