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[matlab] decomp_reconst_full DL : 0: 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
Update : 2025-03-07 Size : 1kb Publisher : ali
[matlab] decomp_reconst_W DL : 0: 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
Update : 2025-03-07 Size : 1kb Publisher : ali
[matlab] decomp_reconst_WU DL : 0: 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
Update : 2025-03-07 Size : 1kb Publisher : ali
[Special Effects] Denoise-Methods DL : 1: 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.
Update : 2025-03-07 Size : 7.99mb Publisher : df