Due to their propensity for stripe noise distortions, infrared remote sensing images present substantial difficulty for interpretation. Our ability to address this issue by offering an easy, efficient, and fast technique for image stripe noise correction is what makes our work unique. Our proposed solution tackles stripe noise by subtracting the mean value along the stripes from the noisy image. Additionally, we leverage the wavelet transform on the average signal to exploit the inherent sparsity of noise in the wavelet domain. This approach not only enhances denoising performance without introducing blurring effects but also enables us to recover image details with remarkable precision, all without the need for intricate algorithms, iterative processes, or training models. To validate the effectiveness of our approach, we conducted evaluations using a dataset of real-world infrared remote sensing images. This dataset encompasses a wide range of examples, featuring both real and artificially induced noise scenarios.

This paper was recommended for publication in its revised form by editorial board member, Zhi Gao.

Keywords:

References

1. L. Zhang, L. Zhang, D. Tao, X. Huang and B. Du, Hyperspectral remote sensing image subpixel target detection based on supervised metric learning, IEEE Trans. Geosci. Remote Sens. 52(8) (2013) 4955–4965. Google Scholar
2. D. Zhu, B. Wang and L. Zhang, Airport target detection in remote sensing images: A new method based on two-way saliency, IEEE Geosci. Remote Sens. Lett. 12(5) (2015) 1096–1100. Google Scholar
3. X. Hao, L. Liu, R. Yang, L. Yin, L. Zhang and X. Li, A review of data augmentation methods of remote sensing image target recognition, Remote Sens. 15(3) (2023) 827. Google Scholar
4. C. Lu, Stripe non-uniformity correction of infrared images using parameter estimation, Infrared Phys. Technol. 107 (2020) 103313. Google Scholar
5. J. E. Ball, D. T. Anderson and C. S. Chan, Comprehensive survey of deep learning in remote sensing: Theories, tools, and challenges for the community, J. Appl. Remote Sens. 11(4) (2017) 042609–042609. Google Scholar
6. S. Routray, P. P. Malla, S. K. Sharma, S. K. Panda and G. Palai, A new image denoising framework using bilateral filtering based non-subsampled shearlet transform, Optik 216 (2020) 164903. Google Scholar
7. C. Wang, M. Li, R. Wang, H. Yu and S. Wang, An image denoising method based on bp neural network optimized by improved whale optimization algorithm, EURASIP J. Wireless Commun. Netw. 2021(1) (2021) 1–22. Google Scholar
8. L. Zhou, G. Sun, Y. Li, W. Li and Z. Su, Point cloud denoising review: From classical to deep learning-based approaches, Graph. Models 121 (2022) 101140. Google Scholar
9. H. Zhang, W. Ni, W. Yan, D. Xiang, J. Wu, X. Yang and H. Bian, Registration of multimodal remote sensing image based on deep fully convolutional neural network, IEEE J. Selected Topics Appl. Earth Obs. Remote Sens. 12(8) (2019) 3028–3042. Google Scholar
10. S. V. M. Sagheer and S. N. George, A review on medical image denoising algorithms, Biomed. Signal process. Control 61 (2020) 102036. Crossref, Google Scholar
11. Y. Ma, B. Wei, P. Feng, P. He, X. Guo and G. Wang, Low-dose CT image denoising using a generative adversarial network with a hybrid loss function for noise learning, IEEE Access 8 (2020) 67519–67529. Google Scholar
12. Y. Yuan, X. Zheng and X. Lu, Spectral–spatial kernel regularized for hyperspectral image denoising, IEEE Trans. Geosci. Remote Sens. 53(7) (2015) 3815–3832. Google Scholar
13. E. Wang, P. Jiang, X. Hou, Y. Zhu and L. Peng, Infrared stripe correction algorithm based on wavelet analysis and gradient equalization, Appl. Sci. 9(10) (2019) 1993. Google Scholar
14. H. Shen, W. Jiang, H. Zhang and L. Zhang, A piece-wise approach to removing the nonlinear and irregular stripes in modis data, Int. J. Remote Sens. 35(1) (2014) 44–53. Google Scholar
15. A. C. Yağan and M. T. Özgen, A spectral graph wiener filter in graph fourier domain for improved image denoising, in 2016 IEEE Global Conf. Signal and Information Processing (IEEE, 2016), pp. 450–454. Google Scholar
16. N. Kapoor, A. Bär, S. Varghese, J. D. Schneider, F. Hüger, P. Schlicht and T. Fingscheidt, From a fourier-domain perspective on adversarial examples to a wiener filter defense for semantic segmentation, in 2021 Int. Joint Conf. Neural Networks (IEEE, 2021), pp. 1–8. Google Scholar
17. M. Kimlyk and S. Umnyashkin, Image denoising using discrete wavelet transform and edge information, in 2018 IEEE Conf. Russian Young Researchers in Electrical and Electronic Engineering (IEEE, 2018), pp. 1823–1825. Google Scholar
18. N. You, L. Han, D. Zhu and W. Song, Research on image denoising in edge detection based on wavelet transform, Appl. Sci. 13(3) (2023) 1837. Google Scholar
19. G. Chen and A. Krzyzak, Invariant pattern recognition with log-polar transform and dual-tree complex wavelet-fourier features, Sensors 23(8) (2023) 3842. Google Scholar
20. Q. Zeng, H. Qin, X. Yan and T. Yang, Fourier domain anomaly detection and spectral fusion for stripe noise removal of TIR imagery, Remote Sens. 12(22) (2020) 3714. Google Scholar
21. H. Xin, Z. Sun, Y. Xing and J. Wang, Bm3d adaptive TV filtering-based convolutional neural network for espi image denoising, Appl. Opt. 60(35) (2021) 10920–10927. Google Scholar
22. J.-H. Yang, X.-L. Zhao, T.-H. Ma, Y. Chen, T.-Z. Huang and M. Ding, Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization, J. Comput. Appl. Math. 363 (2020) 124–144. Google Scholar
23. Y.-C. Heo, K. Kim and Y. Lee, Image denoising using non-local means (NLM) approach in magnetic resonance (MR) imaging: A systematic review, Appl. Sci. 10(20) (2020) 7028. Google Scholar
24. H. Zhang, J. Cai, W. He, H. Shen and L. Zhang, Double low-rank matrix decomposition for hyperspectral image denoising and destriping, IEEE Trans. Geosci. Remote Sens. 60 (2021) 1–19. Google Scholar
25. Y. Chen, T.-Z. Huang, X.-L. Zhao, L.-J. Deng and J. Huang, Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint, Remote Sens. 9(6) (2017) 559. Crossref, Google Scholar
26. J.-L. Wang, T.-Z. Huang, X.-L. Zhao, J. Huang, T.-H. Ma and Y.-B. Zheng, Reweighted block sparsity regularization for remote sensing images destriping, IEEE J. Selected Topics Appl. Earth Observations Remote Sens. 12(12) (2019) 4951–4963. Google Scholar
27. X. Chang, W.-S. Zheng and J. Zhang, Learning person–person interaction in collective activity recognition, IEEE Trans. Image Process. 24(6) (2015) 1905–1918. Google Scholar
28. Y. Chang, L. Yan, T. Wu and S. Zhong, Remote sensing image stripe noise removal: From image decomposition perspective, IEEE Trans. Geosci. Remote Sens. 54(12) (2016) 7018–7031. Google Scholar
29. Y. Chang, L. Yan and S. Zhong, Transformed low-rank model for line pattern noise removal, in Proc. IEEE Int. Conf. Computer Vision (IEEE, 2017), pp. 1726–1734. Google Scholar
30. Y.-J. Sun, T.-Z. Huang, T.-H. Ma and Y. Chen, Remote sensing image stripe detecting and destriping using the joint sparsity constraint with iterative support detection, Remote Sens. 11(6) (2019) 608. Google Scholar
31. J.-H. Yang, X.-L. Zhao, J.-J. Mei, S. Wang, T.-H. Ma and T.-Z. Huang, Total variation and high-order total variation adaptive model for restoring blurred images with cauchy noise, Comput. Math. Appl. 77(5) (2019) 1255–1272. Google Scholar
32. Y. Chang, M. Chen, L. Yan, X.-L. Zhao, Y. Li and S. Zhong, Toward universal stripe removal via wavelet-based deep convolutional neural network, IEEE Trans. Geosci. Remote Sens. 58(4) (2019) 2880–2897. Google Scholar
33. P. Xiao, Y. Guo and P. Zhuang, Removing stripe noise from infrared cloud images via deep convolutional networks, IEEE Photon. J. 10(4) (2018) 1–14. Google Scholar
34. J. Li, X. Wu and Z. Hu, Deep learning for simultaneous seismic image super-resolution and denoising, IEEE Trans. Geosci. Remote Sens. 60 (2021) 1–11. Google Scholar
35. M. Diwakar and M. Kumar, A review on ct image noise and its denoising, Biomed. Signal Process. Control 42 (2018) 73–88. Crossref, Google Scholar
36. A. Haouzia and R. Noumeir, Methods for image authentication: A survey, Multimedia Tools Appl. 39 (2008) 1–46. Google Scholar
37. Y. Wang, L. Wei, L. Yuan, C. Li, G. Lv, F. Xie, G. Han, R. Shu and J. Wang, New generation VNIR/SWIR/TIR airborne imaging spectrometer, in Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, Vol. 10156 (SPIE, 2016), pp. 203–210. Google Scholar
38. U. Sara, M. Akter and M. S. Uddin, Image quality assessment through FSIM, SSIM, MSE and PSNRA comparative study, J. Comput. Commun. 7(3) (2019) 8–18. Google Scholar
39. M. Elhoseny and K. Shankar, Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements, Measurement 143 (2019) 125–135. Crossref, Google Scholar
40. Y. Tendero, J. Gilles, S. Landeau and J. Morel, Efficient single image non-uniformity correction algorithm, in Electro-Optical and Infrared Systems: Technology and Applications VII, Vol. 7834 (SPIE, 2010), pp. 96–107. Google Scholar
41. J. Duran, B. Coll and C. Sbert, Chambolle’s projection algorithm for total variation denoising, Image Process. Line 3 (2013) 311–331. Google Scholar
42. K. He, J. Sun and X. Tang, Guided image filtering, IEEE Trans. Pattern Anal. Mach. Intell. 35(6) (2012) 1397–1409. Google Scholar
43. J. Guan, R. Lai and A. Xiong, Wavelet deep neural network for stripe noise removal, IEEE Access 7 (2019) 44544–44554. Crossref, Google Scholar
44. Y. Tendero, S. Landeau and J. Gilles, Non-uniformity correction of infrared images by midway equalization, Image Process. Line 2 (2012) 134–146. Google Scholar
45. Y. Cao, M. Y. Yang and C.-L. Tisse, Effective strip noise removal for low-textured infrared images based on 1-d guided filtering, IEEE Trans. Circuits Syst. Video Technol. 26(12) (2015) 2176–2188. Google Scholar
46. A. Boutemedjet, C. Deng and B. Zhao, Edge-aware unidirectional total variation model for stripe non-uniformity correction, Sensors 18(4) (2018) 1164. Google Scholar
47. J. Li, D. Zeng, J. Zhang, J. Han and T. Mei, Column-spatial correction network for remote sensing image destriping, Remote Sens. 14(14) (2022) 3376. Google Scholar
48. T. Hastie, R. Tibshirani, J. H. Friedman and J. H. Friedman, The Elements of Statistical Learning: Data mining, Inference, and Prediction, Vol. 2 (Springer, 2009). Crossref, Google Scholar