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Description: Decompose image into subbands, denoise, and recompose again.
fh = decomp_reconst(im,Nsc,Nor,block,noise,parent,covariance,optim,sig)
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)
Version using the Full steerable pyramid (2) (High pass residual
splitted into orientations).
JPM, Univ. de Granada, 5/02
Last Revision: 11/04
- Decompose image into subbands, denoise, and recompose again.
fh = decomp_reconst(im,Nsc,Nor,block,noise,parent,covariance,optim,sig)
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)
Version using the Full steerable pyramid (2) (High pass residual
splitted into orientations).
JPM, Univ. de Granada, 5/02
Last Revision: 11/04
Platform: |
Size: 1024 |
Author: ali |
Hits:
Description:
Decompose image into subbands, denoise using BLS-GSM method, and recompose again.
fh = decomp_reconst(im,Nsc,filter,block,noise,parent,covariance,optim,sig)
im: image
Nsc: number of scales
filter: type of filter used (see namedFilters)
block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood
noise: signal with the same autocorrelation as the noise
parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood
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)
Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero).
JPM, Univ. de Granada, 3/03-
Decompose image into subbands, denoise using BLS-GSM method, and recompose again.
fh = decomp_reconst(im,Nsc,filter,block,noise,parent,covariance,optim,sig)
im: image
Nsc: number of scales
filter: type of filter used (see namedFilters)
block: 2x1 vector indicating the dimensions (rows and columns) of the spatial neighborhood
noise: signal with the same autocorrelation as the noise
parent: include (1) or not (0) a coefficient from the immediately coarser scale in the neighborhood
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)
Version using a critically sampled pyramid (orthogonal wavelet), as implemented in MatlabPyrTools (Eero).
JPM, Univ. de Granada, 3/03
Platform: |
Size: 1024 |
Author: ali |
Hits:
Description: 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
Platform: |
Size: 1024 |
Author: ali |
Hits:
Description: 程序实现了三种去噪算法：BLS-GSM/BM3D/NLM，评价指标为PSNR/SSIM.-Program implements three de-noising algorithm: BLS-GSM/BM3D/NLM, with evaluation of PSNR/SSIM.
Platform: |
Size: 8381440 |
Author: df |
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