Skip to main content

Review of Inpainting Techniques for UAV Images

  • Conference paper
  • First Online:
Proceedings of UASG 2019 (UASG 2019)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 51))

Included in the following conference series:

Abstract

Occupying dead pixels, removing uninterested objects and shadows are often desired in the applications of an UAV to extract the natural and man-made feature boundaries. Image inpainting provides a mean to reconstruct the image. The basic idea behind inpainting methods is to naturally fill in absent or lacking portion of an image by using information from the surrounding area. Applications of this technique include the rebuilding of imperfect photographs and films, elimination of superimposed text, removal/replacement of unwanted objects, redeye correction, image coding. This paper reviews various image inpainting methods like PDE based image inpainting, wavelet-based inpainting, structural inpainting, exemplar-based image inpainting and textural inpainting with their variations. Image inpainting can also be used indirectly in squeezing image where some percentage of the original image is transmitted, and the whole image can be reconstructed on the other end using a pre-trained neural network. The critical reviews of each of these traditional methods along with the latest CNN based techniques are compared and suitability of these techniques for examining or repairing the UAV image is analyzed. In this paper, some of the existing quality assessment metrics like PSNR, MSE, ASVS, BorSal etc.related to image inpainting are also discussed.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ratliff BM, Tyo JS, Boger JK, Black WT, Bowers DL, Fetrow MP (2007) Dead pixel replacement in lwir microgrid polarimeters. Opt Express 15(12):7596–7609

    Article  Google Scholar 

  2. Guillemot C, Le Meur O (2014) Image inpainting: overview and recent advances. IEEE Signal Process Mag 31(1):127–144

    Article  Google Scholar 

  3. Efros AA, Leung TK (1999) Texture synthesis by non-parametric sampling. In: Proceedings of the 7th IEEE international conference on computer vision, vol 2. IEEE, pp 1033–1038

    Google Scholar 

  4. Wei LY, Levoy M (2000) Fast texture synthesis using tree-structured vector quantization. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co, pp 479–488

    Google Scholar 

  5. Bugeau A, Bertalmío M, Caselles V, Sapiro G (2010) A comprehensive framework for image inpainting. IEEE Trans Image Process 19(10):2634–2645

    Article  Google Scholar 

  6. Criminisi A, Pérez P, Toyama K (2004) Region filling and object removal by exemplar based image inpainting. IEEE Trans Image Process 13(9):1200–1212

    Article  Google Scholar 

  7. Drori I, Cohen-Or D, Yeshurun H (2003) Fragment-based image completion. In: ACM Transactions on graphics (TOG), vol 22. ACM, pp 303–312

    Google Scholar 

  8. Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517

    Article  Google Scholar 

  9. Bertalmio M, Vese L, Sapiro G, Osher S (2003) Simultaneous structure and texture image inpainting. IEEE Trans Image Process 12(8):882–889

    Article  Google Scholar 

  10. Starck JL, Elad M, Donoho DL (2005) Image decomposition via the combination of sparse representations and a variational approach. IEEE Trans Image Process 14(10):1570–1582

    Article  Google Scholar 

  11. Komodakis N (2006) Image completion using global optimization. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 1. IEEE, pp 442–452

    Google Scholar 

  12. Sun J, Yuan L, Jia J, Shum HY (2005) Image completion with structure propagation. In: ACM transactions on graphics (ToG), vol 24. ACM, pp 861–868

    Google Scholar 

  13. Fadili JM, Starck JL, Elad M, Donoho DL (2009) Mcalab: Reproducible research in signal and image decomposition and inpainting. Comput Sci Eng 1:44–63

    Google Scholar 

  14. Xu Z, Sun J (2010) Image inpainting by patch propagation using patch sparsity. IEEE Trans Image Process 19(5):1153–1165

    Article  Google Scholar 

  15. Ardis PA, Brown CM, Singhal A (2010) Inpainting quality assessment. J Electron Imaging 19(1):011002

    Article  Google Scholar 

  16. Gupta K, Kazi S, Kong T (2016) Deeppaint: a tool for image inpainting. Google Scholar

    Google Scholar 

  17. Oncu AI, Deger F, Hardeberg JY (2012) Evaluation of digital inpainting quality in the context of artwork restoration. In: European conference on computer vision. Springer, pp 561–570

    Google Scholar 

  18. Venkatesh MV, Sen-ching SC (2010) Eye tracking based perceptual image inpainting quality analysis. In: 2010 IEEE international conference on image processing. IEEE, pp 1109–1112

    Google Scholar 

  19. Schmidt U, Gao Q, Roth S (2010) A generative perspective on mrfs in low-level vision. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 1751–1758

    Google Scholar 

  20. Liu J, Musialski P, Wonka P, Ye J (2013) Tensor completion for estimating missing values in visual data. IEEE Trans Pattern Anal Mach Intell 35(1):208–220

    Article  Google Scholar 

  21. Richard MMOBB, Chang MYS (2001) Fast digital image inpainting. In: Appeared in the proceedings of the international conference on visualization, imaging and image processing (VIIP 2001), Marbella, Spain, pp 106–107

    Google Scholar 

  22. Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image inpainting. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co, pp 417–424

    Google Scholar 

  23. Telea A (2004) An image inpainting technique based on the fast marching method. J Graph Tools 9(1):23–34

    Article  Google Scholar 

  24. Tschumperlé D (2006) Fast anisotropic smoothing of multi-valued images using curvature-preserving pde’s. Int J Comput Vis 68(1):65–82

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Chan TF, Shen J (2001) Nontexture inpainting by curvature-driven diffusions. J Vis Commun Image Represent 12(4):436–449

    Article  Google Scholar 

  27. Shen J, Kang SH, Chan TF (2003) Euler’s elastica and curvature-based inpainting. SIAM J Appl Math 63(2):564–592

    Article  Google Scholar 

  28. Ashikhmin M (2001) Synthesizing natural textures. In: Proceedings of the 2001 symposium on interactive 3D graphics, Citeseer, pp 217–226

    Google Scholar 

  29. Liang L, Liu C, Xu YQ, Guo B, Shum HY (2001) Real-time texture synthesis by patch-based sampling. ACM Trans Graph (ToG) 20(3):127–150

    Article  Google Scholar 

  30. Barnes C, Shechtman E, Goldman DB, Finkelstein A (2010) The generalized patchmatch correspondence algorithm. In: European conference on computer vision. Springer, pp 29–43

    Google Scholar 

  31. Efros AA, Freeman WT (2001) Image quilting for texture synthesis and transfer. In: Proceedings of the 28th annual conference on computer graphics and interactive techniques. ACM, pp 341–346

    Google Scholar 

  32. Barnes C, Shechtman E, Goldman DB, Finkelstein A (2010) Supplementary material for the generalized patchmatch correspondence algorithm. Retrieved from on Sep 9, 6

    Google Scholar 

  33. Bertalmio M, Bertozzi AL, Sapiro G (2001) Navier-stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, vol 1. IEEE, pp I–I

    Google Scholar 

  34. Elad M, Starck JL, Querre P, Donoho DL (2005) Simultaneous cartoon and texture image inpainting using morphological component analysis (mca). Appl Comput Harmon Anal 19(3):340–358

    Article  Google Scholar 

  35. Aujol JF, Ladjal S, Masnou S (2010) Exemplar-based inpainting from a variational point of view. SIAM J Math Anal 42(3):1246–1285

    Article  Google Scholar 

  36. Cheng Q, Shen H, Zhang L, Li P (2014) Inpainting for remotely sensed images with a multichannel nonlocal total variation model. IEEE Trans Geosci Remote Sens 52(1):175–187

    Article  Google Scholar 

  37. Nalawade VV, Ruikar SD Image inpainting using wavelet transform. Int J Adv Eng Technol E-ISSN, 0976–3945

    Google Scholar 

  38. Shen H, Zhang L (2009) A map-based algorithm for destriping and inpainting of remotely sensed images. IEEE Trans Geosci Remote Sens 47(5):1492–1502

    Article  Google Scholar 

  39. Cai N, Su Z, Lin Z, Wang H, Yang Z, Ling BWK (2017) Blind inpainting using the fully convolutional neural network. Vis Comput 33(2):249–261

    Article  Google Scholar 

  40. Xie J, Xu L, Chen E (2012) Image denoising and inpainting with deep neural networks. In: Advances in neural information processing systems. pp 341–349

    Google Scholar 

  41. Hays J, Efros AA (2008) Scene completion using millions of photographs. Commun ACM 51(10):87–94

    Article  Google Scholar 

  42. Dang TT, Beghdadi A, Larabi MC (2013) Visual coherence metric for evaluation of color image restoration. In: 2013 colour and visual computing symposium (CVCS). IEEE, pp 1–6

    Google Scholar 

  43. Ardis PA, Singhal A (2009) Visual salience metrics for image inpainting. In: Visual communications and image processing 2009, vol 7257. W. International Society for Optics and Photonics, p 72571

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Garima Kadian .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kadian, G., Khadanga, G. (2020). Review of Inpainting Techniques for UAV Images. In: Jain, K., Khoshelham, K., Zhu, X., Tiwari, A. (eds) Proceedings of UASG 2019. UASG 2019. Lecture Notes in Civil Engineering, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-37393-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-37393-1_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37392-4

  • Online ISBN: 978-3-030-37393-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics