A SURVEY OF MULTISPECTRAL IMAGE DENOISING METHODS FOR SATELLITE IMAGERY APPLICATIONS

Ankush Rai; Jagadeesh Kannan R

doi:10.22159/ajpcr.2017.v10s1.19740

Authors

Ankush Rai School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India
Jagadeesh Kannan R School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19740

Keywords:

Multispectral image denoising, Satellite images, Learning-based denoising algorithms

Abstract

In comparison with the standard RGB or gray-scale images, the usual multispectral images (MSI) are intended to convey high definition and an
authentic representation for real world scenes to significantly enhance the performance measures of several other tasks involving with computer
vision, segmentation of image, object extraction, and object tagging operations. While procuring images form satellite, the MSI are often prone to
noises. Finding a good mathematical description of the learning-based denoising model is a difficult research question and many different researches
accounted in the literature. Many have attempted its use with the application of neural network as a sparse learned dictionary of noisy patches.
Furthermore, this approach allows several algorithm to optimize itself for the given task at hand using machine learning algorithm. However, in
practices, a MSI image is always prone to corruption by various sources of noises while procuring the images. In this survey, we studied the past
techniques attempted for the noise influenced MSI images. The survey presents the outline of past techniques and their respective advantages in
comparison with each other.

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References

Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans Image Process 2007;16(8):2080-95.

Lansel S. Denoise Lab. Available from: http://www.stanford. edu/~slansel/DenoiseLab.

Katkovnik V, Foi A, Egiazarian K. Astola J. From local kernel to nonlocal multiple-model image denoising. Int J Comput Vis 2010;86:1-32.

Vansteenkiste E, Van der Weken D, Philips W, Kerre E. Perceived image quality measurement of state-of-the-art noise reduction schemes. In: Lecture Notes in Computer Science ACIVS. Vol. 4179. Antwerp, Belgium: Springer; 2006. p. 114-24.

Dabov K, Foi A, Katkovnik V, Egiazarian K. Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: Proceedings IEEE International Conference on Image Processing. Vol. 1. September; 2007. p. 313-6.

Starck J, Candes E, Donoho D. The curvelet transform for image denoising. IEEE Trans Image Process 2002;11:670-84.

Saghri JA, Tescher AG. Near-lossless bandwidth compression for radiometric data. Opt Eng 1991;30(7):934-9.

Epstein B, Hingorani R, Shapiro J, Czigler M. Multispectral KLT-wavelet data compression for landsat thematic mapper images. In: Data Compression Conference, 1992. DCC â€™92. March; 1992. p. 200-8.

Tretter D, Bouman C. Optimal transforms for multispectral and multilayer image coding. IEEE Trans Image Process 1995;4(3):296-308.

Cagnazzo M, Poggi G, Verdoliva L. Region-based transform coding of multispectral images. IEEE Trans Image Process 2007;16(12):2916-26.

Foi A, Trimeche M, Katkovnik V, Egiazarian K. Practical poissonian-gaussian noise modeling and fitting for single-image raw-data. IEEE Trans Image Process 2008;17(10):1737-54.

Foi A. Clipped noisy images: Heteroskedastic modeling and practical denoising. Signal Process 2009;89(12):2609-29.

Hordley S, Finalyson G, Morovic P. A multi-spectral image database and its application to image rendering across illumination, in image and graphics. In: Proceedings Third International Conference on, December; 2004. p. 394-7.

Finlayson G, Hordley S, Morovic P. Using the spectra cube to build a multispectral image database. In: Proceedings Second European Conference on Color in Graphics, Imaging and Vision, CGIV 2004. Aachen, Germany, April; 2004. p. 268-74.

Mahoney M. Text compression as a test for artificial intelligence. In: AAAI/IAAI; 1999. p. 486-502.

Bengio Y, Ducharme R, Vincent P. A neural probabilistic language model. J Mach Learn Res 2003;3:1137-55.

Schwenk H, Gauvain JL. Training neural network language models on very large corpora. In: Proceedings Joint Conference HLT/EMNLP, October; 2005.

Scheunders P, Driesen J. Least squares interband denoising of color and multispectral images. In: Proceedings International Conference on Image Processing ICIP. Singapore 24-27 October; 2004.

Buades A, Coll B, Morel J. On image denoising methods. Technical Report 2004-15. ???: CMLA; 2004.

Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans Image Process 2007;16(8):2080-95.

Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision (ICCV); 1998. p. 839-46.

Gerig G, Kubler O, Kikini R, Jolesz FA. Nonlinear anisotropic filtering of MRI data. IEEE Trans Med Imaging 1992;11(2):221-32.

Portilla J, Strela V, Wainwright MJ, Simoncelli EP. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans Image Process 2003;12(11):1338-51.

Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. Trans Image Process 2006;15(12):736-45.

Roth S, Black M. Fields of experts: A framework for learning image