Abstract
Telemedicine imaging services usually require wireless transmission of a large number of medical images MRI/CT, etc., in the network, which are subject to noise interference and block effect during transmission and compression, leading to degradation of image reconstruction quality, thus affecting diagnostic accuracy. In this paper, iterative shrinkage thresholding (ISTA)-based anti-noise compressive perceptual image reconstruction network is proposed to solve the problem. The network adopts a constrained sparse model, which incorporates both orthogonal and binary constraints of the sampling matrix into the network; in addition, the network adopts feature extraction subnetwork, parameter initialization subnetwork, and reconstruction anti-noise subnetwork for compressed perceptual image reconstruction, incorporates the channel attention mechanism, and proposes a hybrid network for anti-noise deblocking in the reconstruction anti-noise subnetwork. Experiments show that the maximum peak signal-to-noise ratio achievable by the network is in the range of 29.70–39.31 dB under different noise interference scenarios.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03095-3/MediaObjects/11760_2024_3095_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03095-3/MediaObjects/11760_2024_3095_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03095-3/MediaObjects/11760_2024_3095_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03095-3/MediaObjects/11760_2024_3095_Fig4_HTML.png)
Similar content being viewed by others
Data availability
Not applicable.
References
Gupta, S., Sunkaria, R.K.: Real-time salt and pepper noise removal from medical images using a modified weighted average filtering. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6 (2017)
Geng, M., Meng, X., Jiangyuan, Yu., Zhu, L., **, L., Jiang, Z., Qiu, B., Li, H., Kong, H., Yuan, J., Yang, K., Shan, H., Han, H., Yang, Z., Ren, Q., Yanye, L.: Content-noise complementary learning for medical image denoising. IEEE Trans. Med. Imaging 41(2), 407–419 (2022)
Yang, Y., Liu, F., Li, M., **, J., Weber, E., Liu, Q., Crozier, S.: Pseudo-polar Fourier transform-based compressed sensing MRI. IEEE Trans. Biomed. Eng. 64(4), 816–825 (2017)
Pan, Y., Liu, M., **a, Y., Shen, D.: Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6839–6853 (2022)
Zhang, S., Qi, L., Li, X., Liang, Z., Sun, X., Liu, J., Lijun, L., Feng, Y., Chen, W.: Mri information-based correction and restoration of photoacoustic tomography. IEEE Trans. Med. Imaging 41(9), 2543–2555 (2022)
**e, Z., Liu, L.: Transferring deep gaussian denoiser for compressed sensing MRI reconstruction. IEEE Multimed. 29(4), 5–13 (2022)
Chowdhury, D., Panda, S., Dutta, S.: Eradication of salt and pepper noise from a tumorous MRI image using SNPRB filter. In: 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), pp. 1–6 (2019)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Sankaranarayanan, A.C., Studer, C., Baraniuk, R.G.: Cs-muvi: video compressive sensing for spatial-multiplexing cameras. In: 2012 IEEE International Conference on Computational Photography (ICCP), pp. 1–10 (2012)
Ambrosanio, M., Pascazio, V.: Three-dimensional subsurface imaging of weak scatterers by using compressive sampling. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1056–1059 (2015)
Zhang, J., Ghanem, B.: Ista-net: interpretable optimization-inspired deep network for image compressive sensing. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1828–1837 (2018)
Zhang, J., Zhao, C., Gao, W.: Optimization-inspired compact deep compressive sensing. IEEE J. Select. Top. Signal Process. 14(4), 765–774 (2020)
You, D., **e, Zhang, J.: Ista-net++: flexible deep unfolding network for compressive sensing. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2021)
Song, J., Chen, B., Zhang, J.: Memory-augmented deep unfolding network for compressive sensing. In: Proceedings of the 29th ACM International Conference on Multimedia, MM ’21. ACM (2021)
**ang, J., Dong, Y., Yang, Y.: Fista-net: learning a fast iterative shrinkage thresholding network for inverse problems in imaging. IEEE Trans. Med. Imaging 40(5), 1329–1339 (2021)
Zhang, Z., Liu, Y., Liu, J., Wen, F., Zhu, C.: Amp-net: denoising-based deep unfolding for compressive image sensing. IEEE Trans. Image Process. 30, 1487–1500 (2021)
**ang, J., Zang, Y., Jiang, H., Wang, L., Liu, Y.: Soft threshold iteration-based anti-noise compressed sensing image reconstruction network. In: Signal Image and Video Processing (2023)
Liu, Z., Meng, X., Liu, H., **e, J., Zhang, D., Tao, Y., Ze, L.: Research on deblurring method for insulator images based on channel and spatial attention mechanisms. In: 2023 IEEE International Conference on Power Science and Technology (ICPST), pp. 317–321 (2023)
Bourtsoulatze, E., Kurka, B.D., Gündüz, D.: Deep joint source-channel coding for wireless image transmission. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4774–4778 (2019)
Chowdhury, D., Panda, S., Dutta, S.: Soutam eradication of salt and pepper noise from a tumorous MRI image using SNPRB filter. In: 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), pp. 1–6 (2019)
Author information
Authors and Affiliations
Contributions
Material preparation, data collection, and analysis were performed by [Jianhong **ang], [Qiming Liang], [Yang Liu] and [Hao Xu]. The first draft of the manuscript was written by [Linyu Wang],and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethical approval
Not applicable.
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.
About this article
Cite this article
**ang, J., Liang, Q., Xu, H. et al. Iterative shrinkage thresholding-based anti-multi-noise compression perceptual image reconstruction network. SIViP 18, 4569–4578 (2024). https://doi.org/10.1007/s11760-024-03095-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-024-03095-3