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    "# Convolutional Sparse Coding\n",
    "\n",
    "## Basic Usage\n",
    "\n",
    "### Greyscale Images\n",
    "\n",
    "-   [Convolutional sparse coding (ADMM solver)](cbpdn_gry.ipynb)\n",
    "-   [Convolutional sparse coding using the parallel ADMM\n",
    "    solver](parcbpdn_gry.ipynb)\n",
    "-   [Convolutional sparse coding using the CUDA solver](cbpdn_cuda.ipynb)\n",
    "-   [Convolutional sparse coding (PGM solver)](cbpdn_pgm_gry.ipynb)\n",
    "-   [Convolutional sparse coding (constrained data\n",
    "    fidelity)](cminl1_gry.ipynb)\n",
    "-   [Convolutional sparse coding (constrained penalty\n",
    "    term)](cprjl1_gry.ipynb)\n",
    "-   [Convolutional sparse coding with gradient penalty using the CUDA\n",
    "    solver](cbpdn_grd_cuda.ipynb)\n",
    "-   [Convolutional sparse coding with lateral\n",
    "    inhibition](cbpdnin_gry.ipynb)\n",
    "-   [Convolutional sparse coding with weighted lateral\n",
    "    inhibition](cbpdnin_wgt_gry.ipynb)\n",
    "\n",
    "### Colour Images\n",
    "\n",
    "-   [Convolutional sparse coding of a colour image with a colour\n",
    "    dictionary](cbpdn_clr_cd.ipynb)\n",
    "-   [Convolutional sparse coding of a colour image with a colour\n",
    "    dictionary (PGM solver)](cbpdn_pgm_clr.ipynb)\n",
    "-   [Convolutional sparse coding of a colour image with a greyscale\n",
    "    dictionary](cbpdn_clr_gd.ipynb)\n",
    "-   [Convolutional sparse coding of a colour image with a greyscale\n",
    "    dictionary and a joint sparsity term](cbpdn_jnt_clr.ipynb)\n",
    "-   [Convolutional sparse coding of a colour image with a product\n",
    "    dictionary](cbpdn_clr_pd.ipynb)\n",
    "\n",
    "## Image Restoration Applications\n",
    "\n",
    "### Denoising (Gaussian White Noise)\n",
    "\n",
    "-   [Remove Gaussian white noise from a greyscale image using\n",
    "    convolutional sparse coding](gwnden_gry.ipynb)\n",
    "-   [Remove Gaussian white noise from a colour image using convolutional\n",
    "    sparse coding](gwnden_clr.ipynb)\n",
    "\n",
    "### Denoising (Impulse Noise)\n",
    "\n",
    "-   [Remove salt & pepper noise from a colour image using convolutional\n",
    "    sparse coding with a colour dictionary](implsden_clr.ipynb)\n",
    "-   [Remove salt & pepper noise from a colour image using convolutional\n",
    "    sparse coding with an l1 data fidelity term and an l2 gradient term,\n",
    "    with a colour dictionary](implsden_grd_clr.ipynb)\n",
    "-   [Remove salt & pepper noise from a hyperspectral image using\n",
    "    convolutional sparse coding with an l1 data fidelity term and an l2\n",
    "    gradient term, with a dictionary consisting of the product of a\n",
    "    convolutional dictionary for the spatial axes and a standard\n",
    "    dictionary for the spectral axis](implsden_grd_pd_dct.ipynb)\n",
    "-   [Remove salt & pepper noise from a hyperspectral image using\n",
    "    convolutional sparse coding with an l1 data fidelity term and an l2\n",
    "    gradient term, with a dictionary consisting of the product of a\n",
    "    convolutional dictionary for the spatial axes and a PCA basis for\n",
    "    the spectral axis](implsden_grd_pd_pca.ipynb)\n",
    "\n",
    "### Inpainting\n",
    "\n",
    "-   [Inpainting of randomly distributed pixel corruption with lowpass\n",
    "    image components handled via non-linear filtering (greyscale\n",
    "    image)](cbpdn_ams_gry.ipynb)\n",
    "-   [Inpainting of randomly distributed pixel corruption with lowpass\n",
    "    image components handled via gradient regularisation of an impulse\n",
    "    dictionary filter (greyscale image)](cbpdn_ams_grd_gry.ipynb)\n",
    "-   [Inpainting of randomly distributed pixel corruption (greyscale\n",
    "    image)](cbpdn_md_gry.ipynb)\n",
    "-   [Inpainting of randomly distributed pixel corruption (greyscale\n",
    "    image) using the parallel ADMM solver](parcbpdn_md_gry.ipynb)\n",
    "-   [Inpainting of randomly distributed pixel corruption (colour\n",
    "    image)](cbpdn_ams_clr.ipynb)"
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