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 The modified Geman & Yang sampler

We use a half-quadratic form of :  auxiliary variable b, auxiliary images Bx, By (gradients w.r.t.  x and y) sampling triplets (X, Bx, By) in an alternated way on X and Bx, By the pixels of Bx and By are independent
and the law of X knowing Bx, By is Gaussian sampling X  is made in a single pass (unlike Gibbs and Metropolis samplers),
the processing is independent of the neighbourhood size
which provides a fast algorithm for posterior distribution sampling. Posterior distribution Posterior modified Geman & Yang algorithm Initialization :  We first calculate the Fourier transform F[h4 ] and DCT[Y], and W : The initialization is done using the reconstructed image, obtained with the ICM-DCT algorithm : X0 = . Sampling Bx,By with a fixed X The pixels b of Bx and By are independent :  Sampling X with fixed Bx,By The distribution of X with known B is Gaussian because  diagonalization of the covariance matrix             by a cosine transform  (DCT)            which enables to fulfill symmetric boundary conditions where R is an image whose pixels are Gaussian random variables,  with variance 1/2.

Example (64x64 image) :

 X0 initialization  X0 = X B Bx 0, By 0  B X iteration 1 X1 X B Bx 1, By 1  B X iteration 2 X2  etc. Ni iterations for initialization before reaching the equilibrum distribution Prior distribution Prior modified Geman & Yang algorithm Initialization :  We first calculate the Fourier transform F[h4 ] and W0 : The initialization is done using a constant image : X0 = 0. Sampling Bx,By with a fixed X The pixels b of Bx and By are independent :  Sampling X with fixed Bx, By  The distribution of X with a known B is Gaussian because  diagonalization of the covariance matrix             by a cosine transform  (DCT)            which enables to fulfill symmetric boundary conditions where R is an image whose pixels are Gaussian random variables,  with variance 1/2.

Example (64x64 sample) :

 X0 initialization  X0 = 0 X B Bx 0, By 0  B X iteration 1 X1 X B Bx 1, By 1  B X iteration 2 X2  etc. Ni iterations for initialization before reaching the equilibrum distribution André Jalobeanu - 24 Aug 1998