CONTRAST IMPROVEMENT TECHNIQUE SATELLITE IMAGES APPLYING FILTRATION METHOD

Rajneesh Pachouri

Abstract


In this paper introduces advanced contrast technology considering. The input images taken from remote Satellites are used in many applications but the images taken are not very enhanced and may contain blurry or less contrast. Despite the growing demand for better remote sensing pictures different methodology was proposed but they are not able to preserve the edge details and Saturation of high and low brightness images areas. Histogram equalization (HE) was the most familiar approach to raising the contrast in various applications. But cannot maintain the shape information and cannot preserve the average Image brightness, which may be lower or higher than the reprocessed image saturation. The suggested algorithm solves this type problem by using effective techniques used for enhanced satellite image contrast using the atomization resolution of atomization of dominant brightness level, ADT function and smoothing of boundary. Experimental results show, that the suggested method rise the contrast and the perspective of the local details that is improved than current techniques and retains poor image information. The advanced approach can definitely improve any depressed contrast images and maintain the edge contingent Purchased with a satellite camera and are also suitable for other imaging devices such as user digital cameras, and compression cameras.


Keywords


Histogram, Curvelet, Discrete wavelet transform (DWT), Contrast enhancement, Remote sensing image.

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References


R. C. Gonzalez, and R. E. Woods, Digital Image Processing, 2nd ed., New Jersey: Prentice Hall, 2002.

H.Demirel, G. Anbarjafari, and M. Jahromi, “Image equalization based on singular value decomposition”, IEEE in proceedings. 23rd International Symposium. Comput. Inf. Sci., Istanbul, Turkey, pp. 1–5, Oct. 2008.

E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, “Photographic tone reproduction for digital images,” in Proc. SIGGRAPH Annu. Conf. Comput. Graph, pp. 249–256, Jul. 2002.

L.Meylan and S. Susstrunk, “High dynamic range image rendering with a retinex-based adaptive filter,” IEEE Transactions on image processing, VOL. 15, NO. 9, pp. 2820–2830, sept. 2006.

S. Chen and A. Beghdadi, “Nature rendering of color image based on retinex,” in proceedings IEEE International Conference Image Process, pp. 1813–1816,Nov. 2009.

Renoh C Johnson, Veena Paul, Naveen N, Padmagireesan S J, “Curvelet Transform based Retinal Image Analysis”, Vol. 3, No. 3, pp. 366–371, June 2013.

Emmanuel Candes, Laurent Demanet, David Donoho and Lexing Ying, “Fast Discrete Curvelet Transforms”, Applied and Computational Mathematics, Caltech, Pasadena, CA 91125, July 2005, revised Mar. 2006.

Eunsung Lee, Sangjin Kim, Wonseok Kang, Doochun Seo, and Joonki Paik, “Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images”, IEEE Geoscience and remote sensing letters, Vol. 10, No. 1, Jan. 2013.

Yeong-Tage Kim, “Contrast enhancement using brightness preserving bi-histogram equalization”, IEEE Trans. Consum. Electron, vol. 43, no. 1, pp. 1–8, Feb. 1997.

Yu Wang, Qian Chen, and Baomin Zhang, “Image enhancement based on equal area dualistic sub-image histogram equalization method”, IEEE Transactions on Consumer Electronics, vol. 45, No. 1, Feb. 1999.

Soong-Der Chen, and Abd. Rahman Ramli, “Minimum mean brightness error bi-histogram equalization in contrast enhancement,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1310-1319, Nov. 2003.

S.-D. Chen, and A. R. Ramli, “Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp.1301-1309, Nov. 2003.

D. Menotti, L. Najman, J. Facon, and A. A. Araujo, “Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving”, Recursively Separated and Weighted Histogram Equalization For Brightness Preservation and Contrast Enhancement, vol. 53, no. 3, pp. 1186- 1194, Aug 2007.

H. Demirel, C. Ozcinar, and G. Anbarjafari, “Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition,” IEEE Geoscience and remote sensing letters, vol. 7, no. 2, pp. 3333–337, Apr. 2010.

Wei-Ming Ke, Chih-Rung Chen, And Ching-Te Chiu “Bita/Swce: Image Enhancement With Bilateral Tone Adjustment and Saliency Weighted Contrast Enhancement” IEEE Transactions On Circuits And Systems For Video Technology, Vol. 21, No. 3, March 2011.

Nyamlkhagva Sengee, Altansukh Sengee, and Heung-Kook Choi “Image Contrast Enhancement using Bi-Histogram Equalization with Neighborhood Metrics” 2010 IEEE transaction on consumer electronics, Vol 56, no. 4, November 2010.




DOI: https://doi.org/10.26483/ijarcs.v10i5.6480

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