Decompose image into subbands (undecimated wavelet), denoise, and recompose again.

　　　　fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig)

im : image

Nsc: Number of scales

daub_order: Order of the daubechie fucntion used (must be even).

block: size of neighborhood within each undecimated subband.

noise: image having the same autocorrelation as the noise (e.g., a delta, for white noise)

parent: are we including the coefficient at the central location at the next coarser scale?

covariance:　　 are we considering covariance or just variance?

optim:　　　　 for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)

sig: standard deviation (scalar for uniform noise or matrix for spatially varying noise)



Javier Portilla, Univ. de Granada, 3/03

Revised: 11/04 - Decompose image into subbands (undecimated wavelet), denoise, and recompose again.

　　　　fh = decomp_reconst_wavelet(im,Nsc,daub_order,block,noise,parent,covariance,optim,sig)

　　　　im :　　　　 image

　　　　Nsc:　　　　 Number of scales

　　　　daub_order:　Order of the daubechie fucntion used (must be even).

　　　　block:　　　 size of neighborhood within each undecimated subband.

　　　　noise:　　　 image having the same autocorrelation as the noise (e.g., a delta, for white noise)

　　　　parent:　　　are we including the coefficient at the central location at the next coarser scale?

　　　　covariance:　　 are we considering covariance or just variance?

　　　　optim:　　　　 for choosing between BLS-GSM (optim = 1) and MAP-GSM (optim = 0)

　　　　sig:　　　　standard deviation (scalar for uniform noise or matrix for spatially varying noise)



　Javier Portilla, Univ. de Granada, 3/03

　Revised: 11/04