Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter

  • 28 Accesses

Abstract

This paper presents a method to reduce noise and refine detail features of a scene based on an iteratively reweighted least squares method. The performance of the proposed filter, called the iteratively reweighted least squares filter (IRLSF), was compared with the state-of-the-art filters by checking their ability to recover simulated edge models under various degrees of noise contamination. The results of the simulation comparison show that IRLSF is superior to the other filters in terms of its ability to recover the original edge models. To apply IRLSF to real images of a scene captured by a camera, a procedure composed of corner detection, least squares matching, bilinear resampling, and iteratively reweighted least squares is proposed. The experimental results show that IRLSF produces mean images that are effectively denoised, and that its accuracy is less than one half of grey-level-quantization-unit of test images captured by a commercial camera.

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

References

  1. Aurich V, Weule J (1995) Non-linear gaussian filters performing edge preserving diffusion. In: Mustererkennung Springer, Berlin, Germany, 538–545, DOI: https://doi.org/10.1007/978-3-642-79980-8_63

  2. Dong L, Zhou J, Zhai G (2017) Efficient image sensor noise estimation via iterative re-weighted least squares. Proceedings of IEEE conference on multimedia and expo., July 10–14, Hong Kong, China, DOI: https://doi.org/10.1109/ICME.2017.8019427

  3. Durand F, Dorsey J (2002) Fast bilateral filtering for the display of high-dynamic-range images. ACM Transactions on Graphics 21:257–266, DOI: https://doi.org/10.1145/566570.566574

  4. Harris C, Stephens M (1988) A combined corner and edge detector. Proceedings of the fourth alvey vision conference, Manchester, UK, DOI: https://doi.org/10.5244/C.2.23

  5. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 35:1397–1409, DOI: https://doi.org/10.1109/TPAMI.2012.213

  6. Koch K (1999) Parameter estimation and hypothesis testing in linear models, 2nd edition. Springer, New York, NY, USA, 149–269

  7. Li Z, Zheng J, Zhu Z, Yao W, Wu S (2015) Weighted guided image filtering. IEEE Transactions on Image Processing 24:120–129, DOI: https://doi.org/10.1109/TIP.2014.2371234

  8. Paris S, Durand F (2009) A fast approximation of the bilateral filter using a signal processing approach. International Journal of Computer Vision 81:24–52, DOI: https://doi.org/10.1007/s11263-007-0110-8

  9. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12:629–639, DOI: https://doi.org/10.1109/34.56205

  10. Porikli F (2008) Constant time O(1) bilateral filtering. Proceedings of IEEE conference on computer vision and pattern recognition, June 23–28, Anchorage, AK, USA, DOI: https://doi.org/10.1109/CVPR.2008.4587843

  11. Rasche C (2018) Rapid contour detection for image classification. IET Image Processing 12:532–538, DOI: https://doi.org/10.1049/iet-ipr.2017.1066

  12. Sapiro G, Ringach DL (1996) Anisotropic diffusion of multivalued images with applications to color images. IEEE Transactions on Image Processing 5:1582–1586, DOI: https://doi.org/10.1109/83.541429

  13. Seo S (2017a) Prediction of edge displacement due to image contrast. Photogrammetric Record 32:119–140, DOI: https://doi.org/10.1111/phor.12189

  14. Seo S (2017b) Estimation of edge displacement against brightness and camera-to-object distance. IET Image Processing 11:568–577, DOI: https://doi.org/10.1049/iet-ipr.2016.0796

  15. Seo S (2018a) Edge modeling by two blur parameters in varying contrasts. IEEE Transactions on Image Processing 27:2701–2714, DOI: https://doi.org/10.1109/TIP.2018.2810504

  16. Seo S (2018b) Subpixel edge localization based on adaptive weighting of gradients. IEEE Transactions on Image Processing 27:5501–5513, DOI: https://doi.org/10.1109/TIP.2018.2860241

  17. Seo S (2019) Subpixel line localization with normalized sums of gradients and location linking with straightness and omni-directionality. IEEE Access 7:180155–180167, DOI: https://doi.org/10.1109/ACCESS.2019.2959320

  18. Seo S (2020) Line-detection based on the sum of gradient angle differences. Applied Sciences 10:254, DOI: https://doi.org/10.3390/app10010254

  19. Smith S, Brady JM (1997) SUSAN: A new approach to low level image processing. International Journal of Computer Vision 23:45–78, DOI: https://doi.org/10.1023/A:1007963824710

  20. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. Proceedings of international conference on computer vision, January 7–7, Bombay, India, DOI: https://doi.org/10.1109/ICCV.1998.710815

  21. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13:600–612, DOI: https://doi.org/10.1109/TIP.2003.819861

  22. Yang Q, Tan KH, Ahuja N (2009) Real-time O(1) bilateral filtering. Proceedings of IEEE conference on computer vision and pattern recognition, June 20–25, Miami, FL, USA, DOI: https://doi.org/10.1109/CVPR.2009.5206542

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B02011625).

Author information

Correspondence to Suyoung Seo.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Seo, S. Image Denoising and Refinement Based on an Iteratively Reweighted Least Squares Filter. KSCE J Civ Eng 24, 943–953 (2020). https://doi.org/10.1007/s12205-020-2103-x

Download citation

Keywords

  • Denoising
  • Least squares matching
  • Iteratively reweighted least squares