Skip to main content
SpringerLink
Log in

VMD-DMD coupled data-driven approach for visual saliency in noisy images

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

Abstract

Human visual system is endowed with an innate capability of distinguishing the salient regions of an image. It do so even in the presence of noise and other natural disturbances. Conventional8 computational saliency models in the literature assume that the input images are clean, though an explicit treatment of noise is missing. In this paper, we propose a coupled data-driven approach for estimating saliency map for a noisy input using Variational Mode Decomposition (VMD) and Dynamic Mode Decomposition(DMD. Variational Mode Decomposition (VMD) is a well received technique explored for denoising in the literature. VMD modes with high entropy (randomness) are removed and the residual modes are employed to generate a scalar valued saliency map. The proposed method is compared against seven state-of-the-art methods over a wide range of noise strengths. The submitted approach furnished comparable results with respect to state-of-the art methods for clean and noisy images in terms of various benchmark performance measures.

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

Similar content being viewed by others

References

  1. Achanta R, Hemami S, Estrada F, Susstrunk S “Frequency-tuned salient region detection.” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2009). No. CONF. 2009.

  2. Borji A, Cheng MM, Jiang H, Li J (2015) Salient object detection: A benchmark. IEEE Trans Image Process 24(12):5706

    Article  MathSciNet  Google Scholar 

  3. Borji A, Sihite DN, Itti L (2013) Quantitative analysis of human-model agreement in visual saliency modeling: A comparative study. IEEE Trans Image Process 22(1):55

    Article  MathSciNet  Google Scholar 

  4. Bruce ND, Tsotsos JK (2009) Saliency, attention, and visual search: An information theoretic approach. J Vision 9(3):5

    Article  Google Scholar 

  5. Cheng MM, Mitra NJ, Huang X, Torr PH, Hu SM (2015) Global contrast based salient region detection. IEEE Trans Pattern Anal Machine Intell 37(3):569

    Article  Google Scholar 

  6. Dragomiretskiy K, Zosso D (2015) Two-dimensional variational mode decomposition. In: International workshop on energy minimization methods in computer vision and pattern recognition. Springer, pp 197–208

  7. Goferman S, Zelnik-Manor L, Tal A (2012) A. Tal, Context-aware saliency detection. IEEE Trans Pattern Anal machine Intell 34(10):1915

    Article  Google Scholar 

  8. Grosek J (2013) Robust real-time image processing through dynamic mode decomposition. Ph.D. thesis

  9. Guo C, Zhang L (2010) A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans Image Process 19(1):185

    Article  MathSciNet  Google Scholar 

  10. Hou X, Zhang L (2007) Saliency detection: A spectral residual approach. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–8

  11. Itti L, Koch C, Niebur E (1998) A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Machine Intell 20(11):1254

    Article  Google Scholar 

  12. Jiang H, Wang J, Yuan Z, Wu Y, Zheng N, Li S (2013) Salient object detection: A discriminative regional feature integration approach. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2083–2090

  13. Kim C, Milanfar P (2013) Visual saliency in noisy images. J Vision 13(4):5

    Article  Google Scholar 

  14. Kutz JN, Grosek J, Brunton S, Fu X, Pendergrass S (2017) Using dynamic mode decomposition for real-time background/foreground separation in video. US Patent 9,674,406

  15. Lahmiri S, Boukadoum M (2014) Biomedical image denoising using variational mode decomposition. In: Biomedical circuits and systems conference (BioCAS), 2014 IEEE. IEEE, pp 340–343

  16. Le Meur O, Le Callet P, Barba D (2007) Predicting visual fixations on video based on low-level visual features. Vision Res 47(19):2483

    Article  Google Scholar 

  17. Liu Y, Yang G, Li M, Yin H (2016) Variational mode decomposition denoising combined the detrended uctuation analysis. Signal Process 125:349

    Article  Google Scholar 

  18. Mohan N, Kumar S, Poornachandran P, Soman K (2016) Modified variational mode decomposition for power line interference removal in ecg signals. Int J Elect Comput Eng 6(1):151

    Google Scholar 

  19. Murray N, Vanrell M, Otazu X, Parraga CA (2011) Saliency estimation using a nonparametric low-level vision model. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 433–440

  20. Pankaj D, Kumar SS, Mohan N, Soman K (2016) Image fusion using variational mode decomposition. Indian J Sci Technol 9(45)

  21. Perazzi F, Krähenbühl P, Pritch Y, Hornung A (2012) Saliency filters: Contrast based filtering for salient region detection. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 733–740

  22. Peters RJ, Iyer A, Itti L, Koch C (2005) Components of bottom-up gaze allocation in natural images. Vision Res 45(18):2397

    Article  Google Scholar 

  23. Rother C, Kolmogorov V, Blake A (2004) Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM transactions on graphics (TOG), vol 23. ACM, pp 309–314

  24. Seo HJ, Milanfar P (2009) Static and space-time visual saliency detection by self-resemblance. J Vision 9(12):15

    Article  Google Scholar 

  25. Sikha O, Kumar SS, Soman K (2017) Salient region detection and object segmentation in color images using dynamic mode decomposition. J Computational Sci

  26. Sikha O, Soman K (2019) Multi-resolution dynamic mode decomposition-based salient region detection in noisy images. Signal Image Video Process :1–9

  27. Tavakoli HR, Rahtu E, Heikkilä J (2011) Fast and efficient saliency detection using sparse sampling and kernel density estimation. In: Scandinavian conference on image analysis. Springer, pp 666–675

  28. Wang J, Li S, Jiang H (2015) Salient object segmentation. US Patent 9,042,648

  29. Wang YS, Tai CL, Sorkine O, Lee TY (2008) Optimized scale-and-stretch for image resizing. In: ACM transactions on graphics (TOG), vol 27. ACM, p 118

  30. Yang C, Zhang L, Lu H, Ruan X, Yang MH (2013) Saliency detection via graph-based manifold ranking. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3166–3173

  31. Yang J, Yang MH (2012) Top-down visual saliency via joint crf and dictionary learning. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2296–2303

  32. Zhang J, Malmberg F, Sclaroff S (2019) Unconstrained salient object detection. In: Visual saliency: from pixel-level to object-level analysis. Springer, pp 95–111

  33. Zhang L, Tong MH, Marks TK, Shan H, Cottrell GW (2008) A bayesian framework for saliency using natural statistics. J Vision 8(7):32

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Computational Engineering & Networking (CEN), Department of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

    O. K. Sikha

  2. Center for Computational Engineering & Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

    K. P. Soman & S. Sachin Kumar

Authors
  1. O. K. Sikha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. P. Soman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Sachin Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to O. K. Sikha.

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

Sikha, O.K., Soman, K.P. & Kumar, S.S. VMD-DMD coupled data-driven approach for visual saliency in noisy images. Multimed Tools Appl 79, 1951–1970 (2020). https://doi.org/10.1007/s11042-019-08297-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08297-3

Keywords

Search

Navigation