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Development of Optimal Weight Algorithm for Efficient Application of Dual Tree Complex Wavelet Transform for Resolution Enhancement of Satellite Images
Wavelets have been intensively studied for resolution enhancement of images since the last decade. Subbands of decomposed wavelet images are interpolated and combined using equal weights to form resolutionenhanced images. Using different weights for subbands may provide different information content in the output image. Hence, the weights need to be optimized. Therefore, here a technique is proposed to obtain optimal weight for subbands in dual tree complex wavelet transform for resolution enhancement of satellite images. The proposed approach effectively selects the optimal weights of individual subbands automatically according to the variances of each subband, and achieves better image quality. The technique is applicable on different satellite data, like MODIS and PALSAR.
DT-CWT, Optimal Weights Algorithm, Resolution Enhancement, Satellite Images, Wavelets.
- Chengqi, C., Bin, L. and Ting, M., The application of very high resolution satellite image in urban vegetation cover investigation: a case study of Xiamen City. J. Geogr. Sci., 2003, 13, 265–270.
- Johansen, K., Coops, N. C., Gergel, S. E. and Stange, Y., Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sensing Environ., 2007, 110, 29–44.
- Yuan, Z. X. and Wang, L. M., Application of high-resolution satellite image for seismic risk assessment. In Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, BC, Canada, 1–6 August 2004, Paper No. 3454.
- Temizel, A. and Vlachos, T., Wavelet domain image resolution enhancement using cycle-spinning. Electron. Lett., 2005, 41, 119– 121.
- Temizel, A., Image resolution enhancement using wavelet domain hidden Markov tree and coefficient sign estimation. In 2007 IEEE International Conference on Image Processing, San Antonio, TX, 2007, pp. V-381–V-384.
- Garg, A., Naidu, S. V., Ahmed, T., Yahia, H. and Singh, D., Wavelet based resolution enhancement for low resolution satellite images. In 2014 9th International Conference on Industrial and Information Systems, 2014, pp. 1–5.
- Demirel, H. and Anbarjafari, G., Satellite image resolution enhancement using complex wavelet transform. IEEE Geosci. Remote Sensing Lett., 2010, 7, 123–126.
- Jebadurai, J. and Peter, J. D., SK-SVR: sigmoid kernel support vector regression based in-scale single image super-resolution. Pattern Recognit. Lett., 2017, 94, 144–153.
- Bhat, S., Babu, R. D. R., Rangarajan, K. and K.a, R., An algorithm to estimate scale weights of complex wavelets for effective feature extraction in aerial images. Def. Sci. J., 2014, 64, 549–556.
- Xu, L., Zhang, J. Q. and Yan, Y., A wavelet-based multisensor data fusion algorithm. IEEE Trans. Instrum. Meas., 2004, 53, 1539–1545.
- Sun, T., Wu, F. and Gao, W., Accurately weighting subbands in temporal wavelet transform. In 2006 IEEE International Symposium on Circuits and Systems, Island of Kos, 2006, pp. 4, 3024.
- Nasersharif, B. and Akbari, A., Application of wavelet transform and wavelet thresholding in robust sub-band speech recognition. In 12th European Signal Processing Conference, Vienna, 2004, pp.345–348.
- Wang, Y. and Ruan, Q., Dual-tree complex wavelet transform based local binary pattern weighted histogram method for palmprint recognition. Comput. Inform., 2012, 28, 299–318.
- Ying, T., Debin, Z. and Baihuan, Z., Ear recognition based on weighted wavelet transform and DCT. In 26th Chinese Control and Decision Conference (2014 CCDC), 2014, pp. 4410–4414.
- Hsu, W.-Y. and Sun, Y.-N., EEG-based motor imagery analysis using weighted wavelet transform features. J. Neurosci. Methods, 2009, 176, 310–318.
- Yoshida, H., Zhang, W., Cai, W., Doi, K., Nishikawa, R. M. and Giger, M. L., Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms. In Proceedings, International Conference on Image Processing, Washington, DC, USA, 1995, vol. 3, pp. 152–155.
- Selesnick, I. W., Baraniuk, R. G. and Kingsbury, N. C., The dualtree complex wavelet transform. IEEE Signal Process. Mag., 2005, 22, 123–151.
- Celik, T. and Tjahjadi, T., Image resolution enhancement using dual-tree complex wavelet transform. IEEE Geosci. Remote Sensing Lett., 2010, 7, 554–557.
- Narasimhan, K., Elamaran, V., Kumar, S., Sharma, K. and Abhishek, P. R., Comparison of satellite image enhancement techniques in wavelet domain. Res. J. Appl. Sci. Eng. Technol., 2012, 4, 5492–5496.
- Gao, S., Zhong, Y. and Li, W., Random weighting method for multisensor data fusion. IEEE Sensing J., 2011, 11, 1955–1961.
- Garg, A., Naidu, S. V., Gupta, S., Singh, D., Brodu, N. and Yahia, H., A novel approach for optimal weight factor of DT-CWT coefficients for land cover classification using MODIS data. In IEEE International Geoscience and Remote Sensing Symposium, Beijing, 2016, pp. 4528–4531.
- Okawa, S., Bocchieri, E. and Potamianos, A., Multi-band speech recognition in noisy environments. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 1998, vol. 2, pp. 641–644.
- Cerisara, C. and Fohr, D., Multi-band automatic speech recognition. Comput. Speech Lang., 2001, 15, 151–174.
- Dhekale, R. B., Jadhav, B. D. and Patil, P. M., Satellite image (multispectral) enhancement techniques in wavelet domain: an overview. Int. J. Comput. Appl., 2015, 112, 16–20.
- Harish, K. and Singh, D., Quality assessment of fused image of MODIS and PALSAR. Prog. Electromagn. Res. B, 2010, 24, 191– 221.
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