function_AnisSect_explorer ![new!](..\new.png) |
![Anisotropic Neighborhoods](AnisSect1.png) | ![Neighborhood Explorer](AnisSect2.png) |
Visualization of anisotropic supports Shows the anisotropic neighborhoods (i.e. union of the supports of the adaptive-scale kernels) which are used for estimation in the Anisotropic LPA-ICI algorithms. |
|
demo_CreateLPAKernels utility_DrawLPAKernels |
![LPA Kernels](demoKer0.png) | ![LPA Kernels](demoKer2.png) |
LPA kernel design Creates LPA kernels and draws them. |
|
demo_DenoisingGaussian |
![Denoising](demoDenG0.png) | ![Denoising demo](demoDenG2.png) |
Anisotropic LPA-ICI denoising Performs the Anisotropic LPA-ICI denoising on observations which are contaminated by additive Gaussian white noise. |
|
demo_RecursiveDenoisingGaussian |
![Recursive Anisotropic LPA-ICI](demoDenRG0.png) | ![recursive denoising demo](demoDenRG2.png) |
Recursive Anisotropic LPA-ICI denoising Performs the recursive Anisotropic LPA-ICI denoising on observations which are contaminated by additive Gaussian white noise. |
|
demo_DeblurringGaussian |
![Deblurring](demoDebG0.png) | ![deblurring demo](demoDebG2.png) |
Anisotropic LPA-ICI deconvolution Performs deblurring (deconvolution) from observations which are blurred and noisy. The RI (Regularized Inverse) and RWI (Regularized Wiener Inverse) Deconvolution Algorithm with Anisotropic LPA-ICI adaptive estimate selection is used. |
|
demo_DenoisingSignDepNoise |
![Recursive algorithm](demoDenSD0.png) | ![denoising demo](demoDenSD2.png) |
Recursive Anisotropic LPA-ICI denoising for Signal-Dependent Noise Performs the recursive Anisotropic LPA-ICI denoising on observations which are contaminated by signal-dependent noise (e.g. Poisson, Film-Grain, Speckle). |
|
demo_DeblurringPoissonian |
![Signal-dependent noise](demoDebP1.png) | ![deblurring demo](demoDebP2.png) |
Anisotropic LPA-ICI Poissonian Deconvolution Performs deblurring (deconvolution) from observations which are blurred and noisy. Noise is modeled as a Poisson process. |
|
demo_InverseHalftoning |
![Inverse Halftoning](demoIHT0.png) | ![Inverse-Halftoning demo](demoIHT2.png) |
Anisotropic LPA-ICI Inverse-Halftoning Reconstructs a continuous-tone image from a given error-diffusion halftone image. Inverse-halftoning is performed using the Anisotropic LPA-ICI deconvolution with RI (regularized inverse) and RWI (regularized Wiener inverse) adaptive-scale estimates. |
|
demo_AnisotropicGradient |
![edges](demoRie1.png) | ![Anisotropic Gradient demo](demoRie2.png) |
Demonstrates the Anisotropic Gradient concept using the Riemann surface example. |
|
demo_CreateMRLPAKernels |
![MR kernel](demoMRK1.png) | ![MR kernels](demoMRK2.png) |
Multiresolution LPA kernel design Creates and draws multiresolution (MR) LPA two-dimensional kernels. |
|
demo_MR_FilteringGaussian |
![MR variance map](demoMRD1.png) | ![Multiresolution denoising demo](demoMRD2.png) |
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. |