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ROUSSEAU et al.: STRUCTURAL SIMILARITY MEASURE TO ASSESS IMPROVEMENT BY NOISE 39 Fig. 4. (a) Gray-level input image . (b)–(f) gray-level image at the output of the sensor of (6) with saturation level a HXP, with the noise zero-mean Gaussian of rms amplitude '. (b) ' aHmaximizing the cross-correlation g@Y A and minimizing the rms error i@Y A in Fig. 3. (c) ' aHXI. (d) ' aHXPV maximizing the SSIM index ƒ@Y A in Fig. 3. (e) ' aHXS. (f) ' aI. REFERENCES [1] Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett., vol. 9, pp. 81–84, 2002. [2] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, pp. 600–612, 2004. [3] B. Andò and S. Graziani, “Adding noise to improve measurement,” IEEE Instrumen. Meas. Mag., vol. 4, no. 1, pp. 24–30, Mar. 2001. [4] G. P. Harmer, B. R. Davis, and D. Abbott, “A review of stochastic resonance: Circuits and measurement,” IEEE Trans. Instrum. Meas., vol. 51, pp. 299–309, 2002. [5] F. Chapeau-Blondeau and D. Rousseau, “Noise improvements in stochastic resonance: From signal amplification to optimal detection,” Fluct. Noise Lett., vol. 2, pp. L221–L233, 2002. [6] E. Simonotto, M. Riani, C. Seife, M. Roberts, J. Twitty, and F. Moss, “Visual perception of stochastic resonance,” Phys. Rev. Lett., vol. 78, pp. 1186–1189, 1997. [7] F. Vaudelle, J. Gazengel, G. Rivoire, X. Godivier, and F. Chapeau- Blondeau, “Stochastic resonance and noise-enhanced transmission of spatial signals in optics: The case of scattering,” J. Opt. Soc. Amer. B, vol. 15, pp. 2674–2680, 1998. [8] M. O. Hongler, Y. L. de Meneses, A. Beyeler, and J. Jacot, “The resonant retina: Exploiting vibration noise to optimally detect edges in an image,” IEEE Trans. Pattern Anal. Machine Intell., vol. 25, pp. 1051–1062, 2003. [9] A. Histace and D. Rousseau, “Constructive action of noise for impulsive noise removal in scalar images,” Electron. Lett., vol. 42, pp. 393–395, 2006. [10] S. Blanchard, D. Rousseau, D. Gindre, and F. Chapeau-Blondeau, “Constructive action of the speckle noise in a coherent imaging system,” Opt. Lett., vol. 32, pp. 1983–1985, 2007. [11] Y. Yang, Z. Jiang, B. Xu, and D. W. Repperger, “An investigation of two-dimensional parameter-induced stochastic resonance and applications in nonlinear image processing,” J. Phys. A, vol. 42, pp. 145207,1–145207,9, 2009. [12] T. Yang, “Adaptively optimizing stochastic resonance in visual system,” Phys. Lett. A, vol. 245, pp. 79–86, 1998. [13] M. Piana, M. Canfora, and M. Riani, “Role of noise in image processing by the human perceptive system,” Phys. Rev. E, vol. 62, pp. 1104–1109, 2000. [14] H. Sasaki, M. Todorokihara, T. Ishida, J. Miyachi, T. Kitamura, and R. Aoki, “Effect of noise on the contrast detection threshold in visual perception,” Neurosci. Lett., vol. 408, pp. 94–97, 2006. [15] L. Gammaitoni, “Stochastic resonance and the dithering effect in threshold physical systems,” Phys. Rev. E, vol. 52, pp. 4691–4698, 1995. [16] F. Chapeau-Blondeau and X. Godivier, “Theory of stochastic resonance in signal transmission by static nonlinear systems,” Phys. Rev. E, vol. 55, pp. 1478–1495, 1997. [17] R. A. Wannamaker, S. P. Lipshitz, and J. Vanderkooy, “Stochastic resonance as dithering,” Phys. Rev. E, vol. 61, pp. 233–236, 2000. [18] Z. Wang, A. C. Bovik, and H. R. Sheikh, “Structural similarity based image quality assessment,” in Digital Video Image Quality and Perceptual Coding, H. R. Wu and K. R. Rao, Eds. Boca Raton, FL: Taylor & Francis, 2006, pp. 225–236, ch. 7. [19] D. M. Rouse and S. S. Hemami, “Understanding and simplifying the structural similarity metric,” in Proc. 15th IEEE Int. Conf. Image Processing, San Diego, CA, Oct. 12–15, 2008, pp. 1188–1191. [20] Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” in Proc. 37th IEEE Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, Nov. 9–12, 2003, vol. 2, pp. 1398–1402. [21] Z. Wang, L. Lu, and A. C. Bovik, “Video quality assessment based on structural distortion measurement,” Signal Process.: Image Commun., vol. 19, pp. 121–132, 2004. [22] D. Kalenova, D. Dochev, V. Bochko, P. Toivanen, and A. Kaarna, “A novel technique of spectral image quality assessment based on structural similarity measure,” in Proc. 3rd Eur. Conf. Color in Graphics, Imaging and Vision, Leeds, UK, June 19–22, 2006, pp. 499–503. [23] A. C. Brooks and T. N. Pappas, “Using structural similarity quality metrics to evaluate image compression techniques,” in Proc. 32nd IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, HI, Apr. 15–20, 2007, vol. 1, pp. 873–876. [24] J. H. Lee and T. Horiuchi, “Image quality assessment for color halftone images based on color structural similarity,” IEICE Trans. Fund. Electron., Commun. Comput. Sci., vol. E91-A, pp. 1392–1399, 2008. Authorized licensed use limited to: Julien Rousseau. Downloaded on November 2, 2009 at 03:46 from IEEE Xplore. Restrictions apply. 145/197
A. HISTACE and D. ROUSSEAU. Constructive action of noise for impulsive noise removal in scalar images. Electronics Letters, vol. 42:393–395, 2006. 146/197
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