Super resolution reconstruction of multispectral images
by
Rafael Molina
University of Granada, Granada, Spain
Coauthors: Javier Mateos (University of Granada, Granada, Spain), A.K. Katsaggelos (Northwestern University, Evanston, IL, USA)

Super resolution reconstruction of images aims at obtaining a high resolution image from a set of low resolution observations [1]. The last few years have also seen an enormous interest in the application of such techniques to compressed or uncompressed video sequences [2]. Very recently, the existing super resolution approaches have started to be applied to multispectral and hyperspectral images. In this work we study the application of Bayesian Super Resolution techniques to multispectral images. Images of different spatial resolution taken in different bands are combined to improve their resolution [3,4]. Experimental results are presented on Landsat 7 Enhanced Thematic Mapper data.

References

[1] Moon Gi Kang; Chaudhuri, S. (eds); Super-resolution image reconstruction. IEEE Signal Processing Magazine, Volume: 20 , Issue: 3 , May 2003.

[2] L.D. Alvarez, R. Molina, and A.K. Katsaggelos, High Resolution Images from a Sequence of Low Resolution Observations, in Digital Image Sequence Processing, Compression and Analysis., T. R. Reed (ed.), ch. 9, pp: 233-259, CRC Press, 2004.

[3] Price, J.C.; Combining multispectral data of differing spatial resolution. IEEE Trans. On Geoscience and Remote Sensing, vol. 37, no 3, pp: 1199 –1203, 1999.

[4] J.H. Park and M.G. Kang, Spatially adaptive multiresolution multispectral image fusion, Int. J. Remote Sensing, vol 25, no 23, pp: 5491-5509, 2004.

Date received: March 12, 2005