Skip to main content
SpringerLink
Log in

An image denoising iterative approach based on total variation and weighting function

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Image denoising is an important technology for image preprocessing. In recent years, the image denoising technology based on total variation (TV) has been rapidly developed. However, However, although it can preserve image details well, which generates obvious staircase effects. This is due to the traditional TV-based image denoising technology only applies the gradient information and ignored the local variance of the image. In order to suppress staircase effect, in this paper, a novel image denoising approach based on TV model and weighting function is proposed. First, the theory mechanism of staircase effect brought by the traditional TV model is analyzed. Second, the effects of weighting function on edge regions, flat regions, and gradation and detail regions are also analyzed. Third, based on the above analysis, an improved TV model is proposed. Finally, the image denoising approach is implemented by an iterative algorithm. The experimental results show that, compared with various state-of-the-art models denoising models, the proposed image denoising approach can effectively suppress the staircase effect of the traditional TV model in most cases, preserve the image details, and improve the image denoising performance.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Chen L X (2010) Study on image restoration models based on PDE and image enhancement and segmentation algorithms. Ph.D. Thesis, Xidian University, Xi’an

  2. Chen D, Chen YQ, Xue D (2015) Fractional-order total variation image denoising based on proximity algorithm. Appl Math Comput 257:537–545

    MathSciNet  MATH  Google Scholar 

  3. Du H, Liu Y (2018) Minmax-concave total variation denoising. SIViP 12(6):1027–1034

    Google Scholar 

  4. Fan D P, Cheng M M, Liu J J, et al. (2018) Salient objects in clutter: bringing salient object detection to the foreground. Eur Conf Comput Vision (ECCV). 186–202

  5. Fu K, Zhao Q, Gu IYH et al (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69–82

    Google Scholar 

  6. Gilboa G, Sochen N, Zeevi YY (2002) Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Trans Image Process 11(7):689–703

    Google Scholar 

  7. Hasan M, El-Sakka MR (2018) Improved BM3D image denoising using SSIM-optimized wiener filter. EURASIP J Image Video Proc 2018(1):25 1-12

    Google Scholar 

  8. He N, Wang JB, Zhang LL et al (2015) An improved fractional-order differentiation model for image denoising. Signal Process 112:180–188

    Google Scholar 

  9. Highnam R, Brady M (1997) Model-based image enhancement of far infrared images. IEEE Trans Pattern Anal Mach Intell 19(4):410–415

    Google Scholar 

  10. Jain P, Tyagi V (2016) A survey of edge-preserving image denoising methods. Inf Syst Front 18(1):159–170

    Google Scholar 

  11. Jiang L, Huang J, Lv XG et al (2015) Alternating direction method for the high-order total variation-based Poisson noise removal problem. Numer Algorithms 69(3):495–516

    MathSciNet  MATH  Google Scholar 

  12. Jin C, Jin SW (2016) Robust digital image watermark scheme on wavelet domain using fuzzy rough sets. J Intel Fuzzy Syst 30(1):245–256

    Google Scholar 

  13. Jin C, Jin SW (2019) Multi-label automatic image annotation approach based on multiple improvement strategies. IET Image Process 13(4):623–633

    Google Scholar 

  14. Jin C, Yan M, Jin SW (2012) An approach to remove impulse noise from a corrupted image. J Opt 15(2):025402

    Google Scholar 

  15. Jin C, Li Q, Jin SW (2019) An adaptive VPDE image denoising model based on structure tensor. Multimed Tools Appl 78(19):28331–28354

    Google Scholar 

  16. Lakestani M, Razzaghi M, Mousavi Z (2016) Combined shearlet shrinkage and total variation minimization for image denoising. Iran J Sci Technol (Sciences) 42:31–37

    MathSciNet  MATH  Google Scholar 

  17. Lu X, Guo Y, Liu N, Wan L, Fang T (2018) Non-convex joint bilateral guided depth upsampling. Multimed Tools Appl 77(12):15521–15544

    Google Scholar 

  18. Lu X, Ma C, Ni B, et al. (2018) Deep regression tracking with shrinkage loss. Eur Conf Comput Vis (ECCV). 353–369

  19. Lu X, Wang W, Ma C, et al. (2019) See more, know more: unsupervised video object segmentation with co-attention siamese networks. IEEE Conf Comput Vis Patt Recog 3623–3632

  20. Ma Q, Dong F, Kong D (2017) A fractional differential fidelity-based PDE model for image denoising. Mach Vis Appl 28(5–6):635–647

    Google Scholar 

  21. Ma K, Duanmu Z, Yeganeh H et al (2017) Multi-exposure image fusion by optimizing a structural similarity index. IEEE Trans Comput Imaging 4(1):60–72

    MathSciNet  Google Scholar 

  22. Ma TH, Huang TZ, Zhao XL (2018) Spatially dependent regularization parameter selection for total generalized variation-based image denoising. Comput Appl Math 37(1):277–296

    MathSciNet  MATH  Google Scholar 

  23. Makovetskii A, Vokhmintsev A, Kober V, et al. (2015) Frequency Analysis of Gradient Descent Method and Accuracy of Iterative Image Restoration. International conference on analysis of images, Social Networks and Texts. Springer, Cham, 114–122

  24. Poobathy D, Chezian RM (2014) Edge detection operators: peak signal to noise ratio based comparison. Int J Image, Graphics Sign Proc 6(10):55–61

    Google Scholar 

  25. Rafsanjani HK, Sedaaghi MH, Saryazdi S (2017) An adaptive diffusion coefficient selection for image denoising. Dig Sign Proc 64:71–82

    MathSciNet  MATH  Google Scholar 

  26. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D: Nonl Phenom 60(1–4):259–268

    MathSciNet  MATH  Google Scholar 

  27. Sara U, Akter M, Uddin MS (2019) Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study. J Comput Commun 7(3):8–18

    Google Scholar 

  28. Shao L, Yan R, Li X et al (2013) From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms. IEEE Trans Cyber 44(7):1001–1013

    Google Scholar 

  29. Shen Y, Liu Q, Lou S et al (2017) Wavelet-based total variation and nonlocal similarity model for image denoising. IEEE Sign Proc Lett 24(6):877–881

    Google Scholar 

  30. Strong D, Chan T (2003) Edge-preserving and scale-dependent properties of total variation regularization. Inverse Problems 19(6):165–187

    MathSciNet  MATH  Google Scholar 

  31. Tang L, Fang Z (2016) Edge and contrast preserving in total variation image denoising. EURASIP J Adv Sign Proc 2016(1):13 1-21

    Google Scholar 

  32. Wang X, Liu Y, Zhang H et al (2015) A total variation model based on edge adaptive guiding function for remote sensing image denoising. Int J Appl Earth Obs Geoinf 34:89–95

    Google Scholar 

  33. Wang JH, Meng FY, Pang LP et al (2017) An adaptive fixed-point proximity algorithm for solving total variation denoising models. Inf Sci 402:69–81

    MathSciNet  MATH  Google Scholar 

  34. Wei J, Wang S, Huang Q (2020) F3Net: fusion, feedback and focus for salient object detection. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 7–12 February New York, USA

  35. Xiaoling G, Jie Y, Xiao Z (2016) An algorithm for image denoising based on adaptive total variation. Frontier Computing. Springer, Singapore, 155–160

  36. Xu J, Feng A, Hao Y et al (2016) Image deblurring and denoising by an improved variational model. AEU-Int J Electron Commun 70(9):1128–1133

    Google Scholar 

  37. Xu J, Hao Y, Song H (2017) A modified LOT model for image denoising. Multimed Tools Appl 76(6):8131–8144

    Google Scholar 

  38. Yan J, Lu WS (2015) Image denoising by generalized total variation regularization and least squares fidelity. Multidim Syst Sign Process 26(1):243–266

    MathSciNet  MATH  Google Scholar 

  39. Yue H, Sun X, Yang J, et al. (2014) CID: combined image denoising in spatial and frequency domains using web images. IEEE Conf Comput Vis Patt Recog 2933–2940

  40. Zhang X, Ye W (2017) An adaptive fourth-order partial differential equation for image denoising. Comput Math Appl 74(10):2529–2545

    MathSciNet  MATH  Google Scholar 

  41. Zhao JX, Cao Y, Fan DP et al. (2019) Contrast prior and fluid pyramid integration for RGBD salient object detection. IEEE Conf Comput Vis Patt Recog 3927–3936

Download references

Author information

Authors and Affiliations

  1. School of Computer, Central China Normal University, Wuhan, 430079, People’s Republic of China

    Cong Jin & Ningli Luan

Authors
  1. Cong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ningli Luan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cong Jin.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, C., Luan, N. An image denoising iterative approach based on total variation and weighting function. Multimed Tools Appl 79, 20947–20971 (2020). https://doi.org/10.1007/s11042-020-08871-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08871-0

Keywords

Search

Navigation