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Abstract: In an image restoration process, to obtain good results is challenging because of the unavoidable existence of noise even if the blurring information is already known. To suppress the deterioration caused by noise during the image deblurring process, we propose a new deblurring method with a known kernel. First, the noise in the measurement process is assumed to meet the gaussian distribution to fit the natural noise distribution. Second, the first and second orders of derivatives are supposed to satisfy the independent gaussian distribution to control the non-uniform noise. Experimental results show that our method is obviously superior to the Wiener filter, regularized filter, and Richardson-Lucy (RL) algorithm. Moreover, owing to processing in the frequency domain, it runs faster than the other algorithms, in particular about six times faster than the RL algorithm.

[1]Chen, X.C., Cao, F.M., Jin, W.Q., 2007. Recursive model of forward motion blurred image based on polar coordinates. Acta Photon. Sin., 36(3):552-556.

[2]Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T., 2006. Removing camera shake from a single photograph. ACM Trans. Graph., 25(3):787-794.

[3]Fu, Z.L., Feng, H.J., Xu, Z.H., Li, Q., Mao, C.J., 2009. Restoration of the image blurred by motion based on high-speed CCD motion detection. Opto-Electron. Eng., 36(3):69-73.

[4]Gonzalez, R.C., Woods, R.E., 1992. Digital Image Processing. Addison-Wesley, New York, NY.

[5]Gonzalez, R.C., Woods, R.E., Eddins, S.L., 2004. Digital Image Processing Using MATLAB. Pearson Prentice Hall, Upper Saddle River, NJ, USA.

[6]Jain, A.K., 1989. Fundamentals of Digital Image. Prentice-Hall, Inc., Upper Saddle River, NJ, USA.

[7]Levin, A., Fergus, R., Durand, F., Freeman, W.T., 2007. Image and depth from a conventional camera with a coded aperture. ACM Tran. Graph., 26(3), Article 70.

[8]Lucy, L., 1974. An iterative technique for technique for the rectification of observed distributions. Astron. J., 79(6):745-754.

[9]Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P., 2003. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans. Image Process., 12(11):1338-1351.

[10]Richardson, W.H., 1972. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am., 62(1):55-58.

[11]Shi, L., Su, X.Q., Xiang, J.B., 2008. An electronic image stabilization method based on feature block matching. Photon J., 37(1):202-205.

[12]Zheng, X.F., Chen, Y.T., Xu, Z.H., 2008. A fast electronic image stabilization algorithm for tranlational and rotational motion compensation. Acta Photon. Sin., 37(9):1890-1894.