function_AnisSect_explorer 
 
Visualization of anisotropic supports Shows the anisotropic neighborhoods (i.e. union of the supports of the adaptivescale kernels) which are used for estimation in the Anisotropic LPAICI algorithms. 

demo_CreateLPAKernels utility_DrawLPAKernels 
 
LPA kernel design Creates LPA kernels and draws them. 

demo_DenoisingGaussian 
 
Anisotropic LPAICI denoising Performs the Anisotropic LPAICI denoising on observations which are contaminated by additive Gaussian white noise. 

demo_RecursiveDenoisingGaussian 
 
Recursive Anisotropic LPAICI denoising Performs the recursive Anisotropic LPAICI denoising on observations which are contaminated by additive Gaussian white noise. 

demo_DeblurringGaussian 
 
Anisotropic LPAICI deconvolution Performs deblurring (deconvolution) from observations which are blurred and noisy. The RI (Regularized Inverse) and RWI (Regularized Wiener Inverse) Deconvolution Algorithm with Anisotropic LPAICI adaptive estimate selection is used. 

demo_DenoisingSignDepNoise 
 
Recursive Anisotropic LPAICI denoising for SignalDependent Noise Performs the recursive Anisotropic LPAICI denoising on observations which are contaminated by signaldependent noise (e.g. Poisson, FilmGrain, Speckle). 

demo_DeblurringPoissonian 
 
Anisotropic LPAICI Poissonian Deconvolution Performs deblurring (deconvolution) from observations which are blurred and noisy. Noise is modeled as a Poisson process. 

demo_InverseHalftoning 
 
Anisotropic LPAICI InverseHalftoning Reconstructs a continuoustone image from a given errordiffusion halftone image. Inversehalftoning is performed using the Anisotropic LPAICI deconvolution with RI (regularized inverse) and RWI (regularized Wiener inverse) adaptivescale estimates. 

demo_AnisotropicGradient 
 
Demonstrates the Anisotropic Gradient concept using the Riemann surface example. 

demo_CreateMRLPAKernels 
 
Multiresolution LPA kernel design Creates and draws multiresolution (MR) LPA twodimensional kernels. 

demo_MR_FilteringGaussian 
 
Anisotropic multiresolution (MR) LPA Denoising Performs the MR anisotropic LPA denoising on observations which are contaminated by additive Gaussian white noise. Multiscale kernels are used for MR signal analysis and thresholding for noise removal. 