| bregman_utils.q | Contains some general routines that are often used in Bregman/Primal Dual
iterative algorithms. These algorithms solve non-linear inverse problems
that are encountered within the field of image reconstruction and restoration.
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| cgn_rand | Generate a colored gaussian random noise, with the given power spectral
density.
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| choose_sparsity_transform | Selection of a sparsity transform
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| combine_frames | Combination of sparsity frames
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| composite_degradation | Composition of degradation operations: A1 o A2 (A1 after A2)
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| correlated_gaussian_noise_degradation | Modeling of correlated Gaussian noise with specified power spectral density
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| covariance_4x4 | Computes an 4x4 covariance matrix (of a 4-component image, across color
channels)
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| covariance_8x8 | Computes an 8x8 covariance matrix for an image with 8 components
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| denoise_nlmeans | Denoising of images using non-local means (assuming additive white Gaussian
input noise)
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| displacement_diff | Computes a displacement difference. Useful for implementing discrete derivatives, such
as used in Total Variation (TV) approaches. The function uses circular boundary
extension.
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| gaussian_blur_degradation | Modeling of a Gaussian blur
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| generate_psd | Generates the power spectral density matrix, corresponding to the
given point-wise function)
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| generic_blur_degradation | Generic blur degradation model
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| kernelshift | Hiep's kernelshift function: pads a given filter kernel (mask) with zeros
so that it has the dimensions rows x cols. This function is useful for
implementing a convolution in the frequency domain
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| L2_CG | Solves the Primal Dual L2-problem using the conjugate gradient method
In particular, the problem:
(lambda * A^H C_inv A + I) x = (lambda * C_inv A^H y + S^H (d + b)
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| L2_FFT | Solves the Primal Dual L2-problem using the FFT method
Note - A_fft and A_fft_H are here specified in FFT domain
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| L2_generic_solver | Obtain a generic solver for the L2 problem
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| L2_pointwise | Solves the Primal Dual L2-problem using the pointwise method
using the point-wise method
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| Noise estimation | |
| primaldual_generic_restoration | Generic image restoration using the primal dual algorithm
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| shrink | Soft-shrinkage of a subband (or cell array)
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| soft_step | A soft step function at x=1 (based on the cosine function)
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| Wavelets | |
| white_gaussian_noise_degradation | Modeling of white Gaussian noise
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