SNR Improvement by Photon Noise Filtering in Ocean Color Monitor Satellite Images

Full Text (PDF, 1316KB), PP.61-67

Views: 0 Downloads: 0


Ashok Kumar 1,* Rajiv Kumaran 1 Harsh C Trivedi 2

1. Space Applications Centre, Indian Space Research Organization, Ahmedabad, India

2. L D College of Engineering, Ahmedabad, India

* Corresponding author.


Received: 22 Oct. 2015 / Revised: 20 Nov. 2015 / Accepted: 5 Jan. 2016 / Published: 8 Feb. 2016

Index Terms

Photon Noise, Signal to Noise Ratio, Ocean Color Monitor, Sigma Filter, Variance stabilization, Root Mean Square Error


In high radiometric resolution electro optical image payloads of remote sensing satellites, photon noise dominates SNR performance. Photon noise is input signal dependent and difficult to filter. This paper proposes a photon noise filtering technique for Ocean Color Monitor (OCM) images. Existing filtering techniques are meant for object detection and handles images with poor SNR. As OCM SNR is on higher side, custom sigma filter based denoising technique is developed. Proposed technique first converts photon noise to signal independent Gaussian noise. For this variance stabilization, Anscombe transform is used. Simulations are carried on various images. Proposed technique provides 20- 50% reduction in overall as well count-wise RMSE. FFT analysis shows significant reduction in noise. Proposed technique is of low complexity.

Cite This Paper

Ashok Kumar, Rajiv Kumaran, Harsh C Trivedi,"SNR Improvement by Photon Noise Filtering in Ocean Color Monitor Satellite Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.2, pp.61-67, 2016. DOI: 10.5815/ijigsp.2016.02.08


[1]Abbas El Gamal, Helmy Eltoukhy, "CMOS image sensors", IEEE circuits and device magazine, May/June 2005, pp 6-20. 

[2]M. Aharon, M. Elad, and A. Bruckstein. "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation" IEEE Trans. Signal Process., 54(11):4311{ 4322, 2006. 

[3]M. Collins, S. Dasgupta, and R. E. Schapire. "A generalization of principal components analysis to the exponential family" In NIPS, pages 617{624, 2002.

[4]K. Dabov, A. Foi, V. Katkovnik, and K. O. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering". IEEE Trans. Image Process., 16(8):2080{ 2095, 2007. 

[5]J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. "Non-local sparse models for image restoration". In ICCV, pages 2272{2279, 2009.

[6]F. J. Anscombe, "The transformation of Poisson, binomial and negative binomial data," Biometrika, vol. 35, pp. 246–254, 1948.

[7]A. Foi. "Optimal inversion of the Anscombe transformation in low-count Poisson image denoising" IEEE Trans. Image Process., 20(1):99{109, 2011.

[8]J. Boulanger, C. Kervrann, P. Bouthemy, P. Elbau, J-B. Sibarita, and J. Salamero. "Patch-based nonlocal functional for denoising uorescence microscopy image sequences" IEEE Trans. Med. Imag., 29(2):442{454, 2010.

[9]B. Zhang, J. Fadili, and J-L. Starck. "Wavelets, ridgelets, and curvelets for Poisson noise removal". IEEE Trans. Image Process., 17(7):1093{1108, 2008.

[10]J. S. Lee, "Digital Image Smoothing and the Sigma Filter", Computer Graphics Image Processing, Vol. 24, 1983, pp. 255-269. 

[11]D. Van De Ville, M. Nachtagael, D. Van der Weken, E. E. Kerre, W. Philips, I. Lemahieu, "Noise Reduction by Fuzzy Image Filtering", IEEE Transactions on Fuzzy Systems, Vol. 11, No. 4, August, 2003, pp. 429-436.

[12]L. Alparone, S. Baronti, A. Garzelli, "A Hybrid Sigma Filter for Unbiased and Edge-Preserving Speckle Reduction", Proceedings of IEEE International Geoscience and Remote Sensing Symposium, IGARS '95, Vol. 2, 1995, pp. 1409-1411.

[13]Radu Ciprian Bilcu Markku Vehvilainen, "A Modified Sigma Filter for Noise Reduction in Images" 

[14]R. C. Bilcu, M. Vehvilainen, "A New Method forNoise Estimation in Images", Proceedings of IEEE-EURASIP International Workshop on Nonlinear Signal and Image Processing, NSIP 2005, May 18-20, 2005, Sapporo, Japan, pp: 290-293.

[15]Ashok Kumar, Rajiv Kumaran, "Implementation of Multi Linear Gain Prior to image compression systems in Remote sensing electro optical payloads" in International Journal of Image, Graphics and Signal Processing, Peer review journal, Feb 2015, 3, pp: 51-57