Skip to main content
Log in

Crow search algorithm with discrete wavelet transform to aid Mumford Shah inpainting model

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Inpainting plays a significant role in solving a variety of image processing issues that comprises zooming, removal of impulse noise, removal of scratches etc. These specified significances are all associated to inpainting in image domain. Even though more advanced inpainting models have been introduced, it suffers from problem of having low quality. Hence this paper intends to develop a novel inpainting model on the basis of MS modeling. Initially, the pre-processing of the image is done by Discrete Waveley Transform (DWT) and further, its given to MS inpainting model. Moreover, the filter coefficient in DWT algorithm is optimized by Crow Search Algorithm (CSA), that is being considered as the main objective. As the resultant image involves more scratches, this proposed model necessitates smoothening image model using Reproducing Kernel Hilbert Smoothing (RKHS). With all these techniques, the proposed inpainting model is termed as Crow Search Optimized DWT Kernel-based MS (CODWTK-MS). During the performance analysis, the proposed method is compared over various traditional inpainting models like MS, DWT-based MS, DWT Kernel-based MS, and Dragonfly Optimized DWT Kernel-based MS (DODWTK-MS) in terms of several measures and proves the superiority of proposed inpainting model.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Liangtian H, Wang Y (2014) Iterative support detection-based split bregman method for wavelet frame-based image inpainting. IEEE J Mag 23(12):5470–5485

    MathSciNet  MATH  Google Scholar 

  2. Barbu T (2016) Variational image inpainting technique based on nonlinear second-order diffusions. Comput Electrical Eng 54:345–353

    Article  Google Scholar 

  3. Suryanarayana M, Muddala M, Sjöström R, Olsson (2016) Virtual view synthesis using layered depth image generation and depth-based inpainting for filling disocclusions and translucent disocclusions. J Vis Commun Image Represent 38:351–366

    Article  Google Scholar 

  4. Zhang H, Dai S (2012) Image inpainting based on wavelet decomposition. Procedia Engineering 29:3674–3678

    Article  Google Scholar 

  5. Cai J-F, Chan RH, Shen Z (2008) A framelet-based image inpainting algorithm. Appl Comput Harmonic Anal 24(02):131–149

    Article  MathSciNet  Google Scholar 

  6. Chen D-Q, Zhou Y, Inexact alternating direction method based on proximity projection operator for image inpainting in wavelet domain. Neurocomputing 189:145–159

    Article  Google Scholar 

  7. Dong B, Ji H, Li J, Shen Z, Xu Y (2012) Wavelet frame based blind image inpainting. Appl Comput Harmon Anal 32(02):268–279

    Article  MathSciNet  Google Scholar 

  8. Huang Ying L, Ming KY (2017) An improved image inpainting algorithm based on image segmentation. Procedia Comput Sci 107:796–801

    Google Scholar 

  9. Ubirat˜a A, Ignacio CR (2007) Block-based image inpainting in the wavelet domain. 23:733–741

  10. Xue H, Zhang S, Cai D (2017) Depth image inpainting: improving low rank matrix completion with low gradient regularization. IEEE J Mag 26(09):4311–4320

    MathSciNet  Google Scholar 

  11. Fei W, Lasith A, Ling P, Roummel F, Marcia RF, Qiu RC (2017) Nonconvex regularization-based sparse recovery and demixing with application to color image inpainting. IEEE J Mag 05:11513–11527

    Google Scholar 

  12. Lai Y, Lan X, Liu Y, Zheng N (November 2016) An efficient depth image-based rendering with depth reliability maps for view synthesis. J Vis Commun Image Represent 41:176–184

    Article  Google Scholar 

  13. Wang M, Yan B, Ngan KN (2013) An efficient framework for image/video inpainting. Image Commun 28(07):753–762

    Google Scholar 

  14. Li M, Wen Y (2012) A new image inpainting method based on TV model. Physics Procedia 33:712–717

    Google Scholar 

  15. Zhang H, Dong Y, Fan Q (2017) Wavelet frame based Poisson noise removal and image deblurring. Signal Process 137:363–372

    Article  Google Scholar 

  16. Zhao L, Bai H, Wang A, Zhao Y, Zeng B (May 2017) Two-stage filtering of compressed depth images with Markov Random Field. Sig Process Image Commun 54:11–22

    Article  Google Scholar 

  17. Amin Sadri FD, Salim Y, Ren M, Zameni T, Sellis, “Shrink: Distance preserving graph compression”, Information Systems, Vol. 69, pp. 180–193, September 2017

    Article  Google Scholar 

  18. Morel J-M, Petro AB, Sbert C (2012) Fourier implementation of Poisson image editing. Pattern Recognit Lett 33(03):342–348

    Article  Google Scholar 

  19. Andrzej Ehrenfeucht G, Rozenberg (2015) Standard and ordered zoom structures. Theor Comput Sci 608(Part 1):4–15

    Article  MathSciNet  Google Scholar 

  20. Demirci S, Baust M, Kutter O, Manstad-Hulaas F, Navab N (2013) Disocclusion-based 2D–3D registration for aortic interventions. Comput Biol Med 43(04):312–322

    Article  Google Scholar 

  21. Vahid K, Alilou F, Yaghmaee (October 2017) Non-texture image inpainting using histogram of oriented gradients. J Vis Commun Image Rep 48:43–53

    Article  Google Scholar 

  22. Haixia Wang L, Jiang R, Liang, Xiao-Xin L (2017) Exemplar-based image inpainting using structure consistent patch matching. Neurocomputing

  23. Yang M, Gadgil N, Comer ML, Delp EJ (2016) Adaptive error concealment for temporal–spatial multiple description video coding. Sig Process Image Commun 47:313–331

    Article  Google Scholar 

  24. Kato T, Hino H, Murata N (2017) Double sparsity for multi-frame super resolution. Neuro-Computing 240:115–126

    Google Scholar 

  25. Chen Z, Dai C, Jiang L, Sheng B, Yuan Y (2016) Structure-aware image inpainting using patch scale optimization. J Vis Commun Image Rep 40:312–323

    Google Scholar 

  26. Zhang F, Chen Y, Xiao Z, Geng L, Wu J, Feng T, Liu P, Tan Y, Wang J (2015) Partial differential equation inpainting method based on image characteristics, ICIG

  27. Selim E, Shen JI (2002) Digital inpainting based on the Mumford–Shah–Euler image model. Eur J Appl Math 13:353–370

    MathSciNet  MATH  Google Scholar 

  28. Bahram Javidi CM, Do S-H, Hong, Nomura T (2006) Multi-spectral holographic three-dimensional image fusion using discrete wavelet transform. J Disp Technol 2:4

    Article  Google Scholar 

  29. Deng L-J, Guo W, Huang T-Z (2015) Single image super-resolution via an iterative reproducing kernel Hilbert space method, IEEE Transactions on Circuits and Systems for Video Technology

  30. Duchon J (1976) “Fonctions-spline et esperances conditionnelles de champs gaussiens,” Ann. Sci. Univ. Clermont Ferrand II Math, pp. 19–27

  31. Duchon J (1977) Splines minimizing rotation-invariant semi-norms in Sobolev spaces. Constructive Theory of Functions of Several Variables, pp 85–100

  32. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  33. Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Expert Syst Appl 79:164–180

    Article  Google Scholar 

  34. Patil B. Hybrid image inpainting using Reproducing kernal Hilbert space and Dragonfly inspired wavelet transform”, In communication

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Balasaheb H. Patil.

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

Patil, B.H., Patil, P.M. Crow search algorithm with discrete wavelet transform to aid Mumford Shah inpainting model. Evol. Intel. 11, 73–87 (2018). https://doi.org/10.1007/s12065-018-0160-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-018-0160-6

Keywords

Navigation