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Balanced GHM Mutiwavelet Transform based Contrast Enhancement Technique for Dark Images using Dynamic Stochastic Resonance



The main aim of this paper is to propose a new technique for enhancing the contrast of dark images using Dynamic Stochastic Resonance (DSR) and Multi Wavelet Transform (MWT), which is computationally more efficient than the conventional methods. In the work, for enhancing the contrast of dark images, the intrinsic noise (darkness) of dark images has been used. The proposed MWT-based DSR scheme (MWT-DSR) can yield better performances in terms of visual information and color preservation than already reported techniques. The desired output response is validated by the Relative Contrast Enhancement Factor (F), Perceptual Quality Measures (PQM) and Color Enhancement Factor (CEF).



Total Pages: 13


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R. Benzi, A. Sutera, and A. Vulpiani, (1981). The mechanism of stochastic resonance, J. Phys. A. 14, 453-457.

L. Bockstein, (1986). Color equalization method and its application to color image processing, J. Opt. Soc. Amer. 3(5), 735-737.

L. Gammaitoni, P. Hanggi and F. Marchesoni, (1998). Stochastic resonance, Rev. Mod. Phys. 70, 223-270.

T. C. Gard, (1998). Introduction to stochastic differential equations" (Marcel-Dekker, New York,).

R. C. Gonzales, & E. Woods (1992). Digital Image Processing. Addison-Wesley.

M. Hongler, Y. Meneses, A. Beyeler, and J. Jacot, (2003). Resonant retina: Exploiting vibration noise to optimally detect edges in an image, IEEE Trans. Pattern Analysis and Machine Intelligence. 25(9), 1051-1062.

Y. U. Huihui, J. I. Ronghua, L. I. Jingyuan and Tiantian Wang, (2012). Color image filtering methods for variable spray systems, Intelligent Automation and Soft Computing. 18(5), 453-460.

R. K. Jha, P. K. Biswas, and B. N. Chatterji, (2012). Contrast enhancement of dark images using stochastic resonance, IET Journal of Image Processing. 6, 230-237.

Jha, Rajib Kumar, Rajlaxmi Chouhan, and P. K. Biswas. "Noise-Induced Contrast Enhancement of Dark Images Using Non-Dynamic Stochastic Resonance." 2012 National Conference on Communications (NCC) (2012): n. pag. Crossref. Web.

D. J. Jobson, Z. Rahman, and G. A. Woodell, (1997). Properties and performance of a center/surround retinex, IEEE Trans. Image Process. 6(3), 451-462.

D. J. Jobson, Z. Rahman and G. A. Woodell, (1997). A multi-scale retinex for bridging the gap between color images and the human observation of scenes, IEEE Trans. Image Process. 6(7), 965-976.

P. Jung, & P. Hanggi, (1991). Amplification of small signal via stochastic Resonance, Phys. Rev. A. 44(12), 8032-8042.

L. Ghouti, Ahmed Bouridane, Mohammad K. Ibrahim and Said Boussakta, (2006). Digital Image Watermarking Using Balanced Multiwavelets, IEEE Transactions on Signal Processing. 54(4), 1519-536.

J. S. Lim, (1990). Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ: Prentice-Hall.

J.-M. Lina, (1997). Complex Daubechies Wavelets: Filters design and applications, in: Proceedings of 1st Conference of International Society for Analysis, its Applications and Computation. 1-18.

J.-M. Lina & M. Mayrand, (2002). Complex Daubechies wavelets, Applied and Computational Harmonic Analysis. 2(3), 219-229.

J. Mukherjee, & S. K. Mitra, (2008) Enhancement of color images by scaling the DCT coefficients, IEEE Transactions on Image Processing. 17(10), 1783-1794.

Renbin Peng et al. "Stochastic Resonance: An Approach for Enhanced Medical Image Processing." 2007 IEEE/NIH Life Science Systems and Applications Workshop (2007): n. pag. Crossref. Web.

C. Rajlaxmi, C. Pradeep Kumar, Rawnak Kumar and Rajib Kumar Jha, (2012). Contrast Enhancement of Dark Images Using stochastic Resonance in Wavelet Domain, International Journal of Machine learning and computing. 2(5), 711-715.

C. Rajlaxmi, Rajib Kumar Jha and Prabir Kumar Biswas, (2012). Enhancement of dark and low-contrast images using dynamic stochastic resonance, IET Image Processing. 7(2), 174-184.

C. Rajlaxmi, Rajib Kumar Jha and Prabir Kumar Biswas, (2012). Wavelet-based Contrast Enhancement of Dark Images using Dynamic Stochastic Resonance, Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP”12).

V. P. S. Rallabandi, (2008). Enhancement of ultrasound images using stochastic resonance based wavelet transform, Computerized medical imaging and graphics. 32, 316-320.

V. P. S. Rallabandi & P. K. Roy, (2010). Magnetic resonance image enhancement using stochastic resonance in Fourier domain, Magnetic Resonance. 28, 1361-1373.

R. M. Rao, & A. S. Bopardikar, (2000). Wavelet Transform-Introduction to Theory and Applications, Pearson Education Inc.

Risken, Hannes. "The Fokker-Planck Equation." Springer Series in Synergetics (1984): n. pag. Crossref. Web.

C. Ryu, S. G. Konga, and H. Kimb, (2011). Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance, Pattern Recognition Letters. 32(2), 107-113.

H. Seddik, Sondes Tebbini and Ezzeddine Ben Braiek, (2014). Smart real time adaptive gaussian filter supervised neural network for efficient gray scale and RGB image denoising, Intelligent Automation and Soft Computing. 20(3), 347-364.

I. E. Selesnick, (2000). Balanced Multiwavelet Bases Based on Symmetric FIR Filters, IEEE Transactions on Signal Processing. 48(1), 184-191.

E. Simonotto, M. Riani, S. Charles, M. Roberts, J. Twitty, and F. Moss, (1997). Visual perception of stochastic resonance, Phys. Rev. Lett. 78(6), 1186-1189.

V. Strela, (1996). Multiwavelets Theory and Applications, Moscow Institute of Physics and Technology.

V. Strela, P. N. Heller, G. Strang, P. Topiwala and C. Heil, (1999). The Application of Multiwavelet Filter Banks to Image Processing, IEEE Trans. on Image Processing. 8(4), 1-30.

R. N. Strickland, C. S. Kim, and W. F. McDonnell, (1987). Digital color image enhancement based on the saturation component, Opt. Eng. 26(7), 609-616.

J. Tang, E. Peli, and S. Acton, (2003). Image enhancement using a contrast measure in the compressed domain, IEEE Signal Process. Lett. 10(10), 289-292.

Z. Wang, H. R. Sheikh, and A. C. Bovik, (2002). No-reference perceptual quality assessment of jpeg compressed images, In Proc. IEEE Int. Conf. Image Processing. 1, 477-480.

S. Wolf, R. Ginosar, and Y. Zeevi, (1998). Spatio-chromatic image enhancement based on a model of human visual information system, J. Vis. Commun. 9(1), 25-37.

Q. Ye, H. Huang, and C. Zhang, (2004). Image enhancement using stochastic resonance, In Proc. IEEE Int. Conf. Image Processing. 1, 263-266.

Q. Ye, H. Huang, X. He and C. Zhang, (2003). A SR-based radon transform to extract weak lines from noise images, Proc. IEEE Int. Conf. on Image Processing, Barcelona, Spain. 5(6), 1849-1852.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present


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