[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Fractional derivative based Unsharp masking approach for enhancement of digital images

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

Abstract

Image visual quality is severely degraded due to various environmental conditions, thus, leading to the loss in image details. Therefore, an image enhancement approach is required to improve the visual quality of images. In this paper, Unsharp Masking (UM) approach based on Riemann-Liouville (RL), Grunwald-Letnikov (GL), and Riesz fractional derivatives is proposed for the image enhancement. The fractional derivatives based UM approach sharpened the edges of an image while preserving its low and medium frequency details. Furthermore, the extra parameter of fractional derivative provides an additional degree of freedom, thus, increasing the effectiveness of the proposed approach. Extensive simulations carried out on several standard images of different sizes validated the performance of proposed approach in comparison to the existing techniques. The capability of the proposed approach is further confirmed by considering the test images with varying illumination conditions. Moreover, the comparative analysis performed in terms of quantitative measures such as Information Entropy (IE), Average Gradient (AG), Measure of Enhancement (EME), etc. confirmed that the proposed UM approach based on Riesz fractional derivative outperforms the existing state-of-the-art image enhancement techniques. Furthermore, the potential of the proposed approach is validated by considering its application in the medical images.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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. Aysal TC, Barner KE (2006) Quadratic weighted median filters for edge enhancement of noisy images. IEEE Trans Image Process 15(11):3294–3310

    Article  Google Scholar 

  2. Budai A, Bock R, Maier A, Hornegger J, Michelson G (2013) Robust Vessel Segmentation in Fundus Images. Int J Biomed Imag 2013:1–11. https://doi.org/10.1155/2013/154860

    Article  Google Scholar 

  3. Chen S, Zhao F (2018) The adaptive fractional order differential model for image enhancement based on segmentation. Int J Pattern Recognit Artif Intell 32:1854005. https://doi.org/10.1142/S0218001418540058

    Article  MathSciNet  Google Scholar 

  4. Chwyl B, Chung AG, Li FY, Wong A, Clausi DA (2015) TIGER: a texture-illumination guided energy response model for illumination robust local saliency. Proc IEEE Int Conf image process (ICIP):1970–1974

  5. Gan Z, Yang H (2010) Texture enhancement though multiscale mask based on RL fractional differential. Proc Int Conf Inf Net Autom (ICINA) 333–337

  6. Garg V, Singh K (2012) An improved Grunwald-Letnikov fractional differential mask for image texture enhancement. Int J Adv Comput Sci Appl 3(3):130–135

    Google Scholar 

  7. Gonzalez RC, Woods RE (2008) Digital image processing. Englewood Cliffs, Prentice Hall

    Google Scholar 

  8. Hemalatha S, Anouncia SM (2018) GL fractional differential operator modified using auto-correlation function: texture enhancement in images. Ain Shams Eng J 9(4):1689–1704

    Article  Google Scholar 

  9. Jain AK (1989) Fundamentals of digital image processing. Englewood Cliffs, Prentice Hall

    MATH  Google Scholar 

  10. Jindal N, Singh K (2014) Image and video processing using discrete fractional transforms. SIViP 8(8):1543–1553

    Article  Google Scholar 

  11. Justin J, Anoop BN, Williams J (2019) A modified unsharp masking with adaptive threshold and objectively defined amount based on saturation constraints. Multimed Tools Appl 78(8):11073–11089

    Article  Google Scholar 

  12. Kansal S, Tripathi RK (2020) Adaptive geometric filtering based on average brightness of the image and discrete cosine transform coefficient adjustment for gray and color image enhancement. Arab J Sci Eng 45:1655–1668

    Article  Google Scholar 

  13. Kansal S, Purwar S, Tripathi RK (2018) Image contrast enhancement using unsharp masking and histogram equalization. Multimed Tools Appl 77(20):26919–26938

    Article  Google Scholar 

  14. Kau LJ, Lee TL (2014) A three-step approach with adaptive additive magnitude selection for the sharpening of images. The Sci World J 2014:1–15. https://doi.org/10.1155/2014/528696

    Article  Google Scholar 

  15. Kaur K, Jindal N, Singh K (2019) Improved homomorphic filtering using fractional derivatives for enhancement of low contrast and non-uniformly illuminated images. Multimed Tools Appl 78(19):27891–27914

    Article  Google Scholar 

  16. Kwok N, Shi H (2014) Design of unsharp masking filter kernel and gain using particle swarm optimization. Proc Int Cong Image Sign Process 217–222

  17. Kwok N, Shi H, Fang G, Ha Q (2013) Adaptive scale adjustment design of unsharp masking filters for image contrast enhancement. Proc Int Conf Mach Learn Cyber 884–889

  18. Lavín-Delgado JE, Solís-Pérez JE, Gómez-Aguilar JF, Escobar-Jiménez RF (2020) A new fractional-order mask for image edge detection based on Caputo–Fabrizio fractional-order derivative without singular kernel. Circ Syst Sign Process 39(3):1419–1448

    Article  Google Scholar 

  19. Lee SL, Tseng CC (2016) Image sharpening using matrix Riesz fractional order differentiator and discrete sine transform. Proc IEEE Int Conf Consum Electron 1–2

  20. Nandal A, Gamboa-Rosales H, Dhaka A, Celaya-Padilla JM, Galvan-Tejada JI, Galvan-Tejada CE, Martinez-Ruiz FJ, Guzman-Valdivia C (2018) Image edge detection using fractional calculus with feature and contrast enhancement. Circ Syst Sign Process 37(9):3946–3972

    Article  Google Scholar 

  21. Ortigueira MD (2011) Fractional calculus for scientists and engineers. Springer, Dordrecht

    Book  Google Scholar 

  22. Panetta K, Samani A, Agaian S (2014) Choosing the optimal spatial domain measure of enhancement for mammogram images. Int J Biomed Imag 2014:1–8. https://doi.org/10.1155/2014/937849

    Article  Google Scholar 

  23. Polesel A, Ramponi G, Mathews VJ (2000) Image enhancement via adaptive unsharp masking. IEEE Trans Image Process 9(3):505–510

    Article  Google Scholar 

  24. Pu YF, Zhou JL, Yuan X (2010) Fractional differential mask: a fractional differential-based approach for multiscale texture enhancement. IEEE Trans Image Process 19(2):491–511

    Article  MathSciNet  Google Scholar 

  25. Raghunandan KS, Shivakumara P, Jalab HA, Ibrahim RW, Kumar GH, Pal U, Lu T (2017) Riesz fractional based model for enhancing license plate detection and recognition. IEEE Trans Circuits Syst Video Technol 28(9):2276–2288

    Article  Google Scholar 

  26. Saxena R, Singh K (2005) Fractional Fourier transform: a novel tool for signal processing. J Indian Inst Sci 85(1):11–26

    Google Scholar 

  27. Sheikh HR, Wang Z, Cormack L, Bovik AC (2005) LIVE image quality assessment database release 2 (2005). http://live.ece.utexas.edu/research/quality. Accessed 10 September 2019

  28. Singh K, Kapoor R (2014) Image enhancement via median-mean based sub-image-clipped histogram equalization. Optik 125(17):4646–4651

    Article  Google Scholar 

  29. Singh G, Singh K (2017) Improved JPEG anti-forensics with better image visual quality and forensic undetectability. Forens Sci Int 277:133–147

    Article  Google Scholar 

  30. Singh K, Saxena R, Kumar S (2013) Caputo-based fractional derivative in fractional Fourier transform domain. IEEE J Emerg Sel Topics Circ Syst 3(3):330–337

    Article  Google Scholar 

  31. Singh H, Kumar A, Balyan LK, Singh GK (2017) A novel optimally weighted framework of piecewise gamma corrected fractional order masking for satellite image enhancement. Comput Electr Eng 75:245–261

    Article  Google Scholar 

  32. Solomon C, Breckon T (2011) Fundamentals of digital image processing: a practical approach with examples in MATLAB. Wiley-Blackwell, Canterbury

    Google Scholar 

  33. Starovoitov VV, Eldarova EE, Iskakov KT (2020) Comparative analysis of the SSIM index and the Pearson coefficient as a criterion for image similarity. Eurasian J Math Comput Appl 8(1):76–90

    Google Scholar 

  34. Suman S, Jha RK (2017) A new technique for image enhancement using digital fractional-order Savitzky–Golay differentiator. Multidim Syst Sign Process 28(2):709–733

    Article  Google Scholar 

  35. The USC-SIPI Image Database [Online] (2017). Available: http://sipi.usc.edu/database/database.php. Accessed 20 July 2020

  36. Tsafack N, Kengne J, Abd-El-Atty B, Iliyasu AM, Hirota K, Abd El-Latif AA (2020) Design and implementation of a simple dynamical 4-D chaotic circuit with applications in image encryption. Inf Sci 515:191–217

    Article  Google Scholar 

  37. Tseng CC, Lee SL (2014) Design of digital Riesz fractional order differentiator. Signal Process 102:32–45

    Article  Google Scholar 

  38. VIP Illumination Saliency Dataset [Online] (2019) Available: https://uwaterloo.ca/vision-image-processing-lab. Accessed 27 July 2020

  39. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  40. Xie L, Wang G, Zhang X, Xiao B, Zhou B, Zhang F (2014) Remote sensing image enhancement based on wavelet analysis and histogram specification. Proc IEEE Int Conf cloud Comput Intell Syst 55–59

  41. Yang Q, Chen D, Zhao T, Chen Y (2016) Fractional calculus in image processing: a review. Fract Calc Appl Anal 19(5):1222–1249

    Article  MathSciNet  Google Scholar 

  42. Ye W, Ma KK (2018) Blurriness-guided unsharp masking. IEEE Trans Image Process 27(9):4465–4477

    Article  MathSciNet  Google Scholar 

  43. Ying Z, Li G, Ren Y, Wang R, Wang W (2017) A new image contrast enhancement algorithm using exposure fusion framework. Proc Int Conf Comp Anal Images Patt 36–46

  44. Yu Q, Liu F, Turner I, Burrage K, Vegh V (2012) The use of a Riesz fractional differential-based approach for texture enhancement in image processing. ANZIAM J 54:590–607

    Article  MathSciNet  Google Scholar 

  45. Zhuang P, Ding X (2020) Underwater image enhancement using an edge-preserving filtering Retinex algorithm. Multimed Tools Appl 79:17257–17277

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Kanwarpreet Kaur, Neeru Jindal & Kulbir Singh

Authors
  1. Kanwarpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neeru Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kulbir Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kulbir Singh.

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

Kaur, K., Jindal, N. & Singh, K. Fractional derivative based Unsharp masking approach for enhancement of digital images. Multimed Tools Appl 80, 3645–3679 (2021). https://doi.org/10.1007/s11042-020-09795-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09795-5

Keywords

Search

Navigation