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Satellite Image Resolution Enhancement based on Interpolation of Wavelet Domain

V. Shobana

Abstract


Satellite images are being used in many field of research, so it is essential to have high resolution Satellite images. Satellite images are affected by various factors such as absorption, scattering etc in the space, resolution of these images is very low. Wavelet domain based methods have proved themselves as most efficient technique serving for the required purpose. In this work, a new satellite image resolution enhancement technique has been proposed whereby discrete wavelet transform (DWT) is used to decompose the input image into different subbands then the high-frequency subband images and the input low-resolution image have been interpolated, followed by combining all these images to generate a new resolution enhanced image by using inverse DWT. Both qualitative and quantitative analysis proves that resolution is increased and visual quality of the image has been improved.

Keywords


Discrete Wavelet Transform, Interpolation, Resolution Enhancement, Satellite Image

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References


Anbarjafari. G and Demirel. H, “Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image,” ETRI J., vol. 32, no. 3, pp. 390–394, Jun. 2010.

Celik,.T ,Direkoglu, Ozkaramanli .H, Demirel.H, and M. Uyguroglu, “Region-based super-resolution aided facial feature extraction from low-resolution video sequences,” in Proc. IEEE ICASSP, Philadelphia, PA, Mar. 2005, vol. II, pp. 789–792.

Crouse M.S, Nowak R.D, and Baraniuk R.G, “Wavelet-based statistical signal processing using hidden Markov models,” IEEE Trans. Signal Process., vol. 46, no. 4, pp. 886–902, Apr. 1998.

Demirel.H and Anbarjafari.G, “Satellite image resolution enhancement using complex wavelet transform,” IEEE Geosci. Remote Sens. Lett. vol. 7, no. 1, pp. 123–126, Jan. 2010.

Gambardella. And Migliaccio.M, “On the super resolution of microwave scanning radiometer measurements,” IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 796–800, Oct. 2008

Kinebuchi. K, Muresan D.D, and ParksT.W, “Image interpolation using wavelet based hidden Markov trees,” in Proc. IEEE ICASSP, 2001, vol. 3, pp. 7–11.

Li. X and Orchard M.T, “New edge-directed interpolation,” IEEE TransImage Process., vol. 10, no. 10, pp. 1521–1527, Oct. 2001.

Piao. Y, Shin.L, and H. W. Park, “Image resolution enhancement using inter-subband correlation in wavelet domain,” in Proc. IEEE ICIP, 2007, vol. 1, pp. I-445–I-448.

Temizel.A and Vlachos.T, “Image resolution up scaling in the wavelet domain using directional cycle spinning,” J. Electron. Imaging, vol. 14, no. 4, p. 040501, 2005.

Tolpekin V.A and Stein. A, “Quantification of the effects of land cover class spectral separability on the accuracy of Markov-random field-based super resolution mapping,” IEEE Trans. Geosci. Remote Sens., vol. 47,no. 9, pp. 3283–3297, Sep. 2009.

Yi-bo.L, Hong.X, and Sen-yue.Y, “The wrinkle generation method for facial reconstruction based on extraction of partition wrinkle line features and fractal interpolation,” in Proc. 4th ICIG, Aug. 22–24, 2007, pp. 933– 937.


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