Jd = diffusion (J, method, N, K) J: to be the spread of the original image method: ' lin' : linear diffusion (constant c = 1). ' Pm1' : perona-malik, c = exp {- (| grad (J) |/K) ^ 2} ' pm2' : perona-malik, c = 1/{1+ (| grad (J) |/K) ^ 2} ' rmp' : the plural K: edge of the threshold parameter N: number of iterations dt: time step (0 < dt < = 0.25, default 0.2) sigma2: If this parameter is calculated to do the convolution with the Gaussian kernel the gradient diffusion coefficient

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Linear and Nonlinear Diffusions

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