Abstract
In this paper, we introduced a remote sensing image inpainting method based on speed-up generalized morphological component analysis (SGMCA). Due to its capability to represent and separate the morphological diversities, generalized morphological component analysis (GMCA) algorithm is a state-of-the-art image inpainting method. SGMCA algorithm introduced in this paper can accelerate the iterative process of GMCA algorithm. By adding some more assumptions to GMCA algorithm, SGMCA algorithm is proven as a much faster algorithm which can handle very large-scale problems. Several experiments illustrate that SGMCA algorithm can recover the remote sensing images with different patterns of missing pixels. It is even hard to distinguish the original remote sensing image from the recovered image through visual effect. The peak signal to noise ratio and structural similarity indices explain why the salient visual effect is obtained, and confirm the marvelous inpainting capability of SGMCA algorithm. Quantitative analysis on time consumption proves that SGMCA algorithm can greatly improve the iterative speed of GMCA algorithm, indeed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bobin J, Starck J-L, Fadili J, Moudden Y (2007) Sparsity and morphological diversity in blind source separation. IEEE Trans Image Process 16(11):2662–2674
Buckheit JB, Donoho DL (1995) Wavelab and reproducible research. Springer, New York, pp 55–81
Bugeau A, Bertalmio M, Caselles V, Sapiro G (2010) A comprehensive framework for image inpainting. IEEE Trans Image Process 19(10):2634–2645
Cheng Q, Shen H, Zhang L, Li P (2013) Inpainting for remotely sensed images with a multichannel nonlocal total variation model. IEEE Trans Geosci Remote Sens 52(99):1–13
Donoho D, Huo X (2001) Uncertainty principles and ideal atomic decomposition. IEEE Trans Inf Theory 47(7):2845–2862
Gladkova I, Grossberg MD, Shahriar F, Bonev G, Romanov P (2012) Quantitative restoration for MODIS band 6 on Aqua. IEEE Trans Geosci Remote Sens 50(6):2409–2416
Lorenzi L, Melgani F, Mercier G (2011) Inpainting strategies for reconstruction of missing data in VHR images. IEEE Geosci Remote Sens Lett 8(5):914–918
Mendez-Rial R, Calvino-Cancela M, Martin-Herrero J (2012) Anisotropic inpainting of the hypercube. IEEE Geosci Remote Sens Lett 9(2):214–218
Pringle M, Schmidt M, Muir J (2009) Geostatistical interpolation of SLCoff Landsat ETM + images. ISPRS J Photogramm Remote Sens 64(6):654–664
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
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
Acknowledgments
This work was supported by Graduate Students Innovation Foundation of Fudan University.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Yu, C., Chen, X. Speed-up generalized morphological component analysis technology used in remote sensing image inpainting application. Arab J Geosci 8, 1251–1259 (2015). https://doi.org/10.1007/s12517-014-1274-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12517-014-1274-5