RegularizationByDenoising.ipynb

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    "# Beyond regularized image reconstruction\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This notebook intends to show what are the next steps in terms of regularized image reconstruction. We will try to focus especially in a framework that allows the introduction of deep learning in a proper mathematical framework that allows for prior and data fitting mitigation called: regularization by denoising (RED) but not only.\n",
    "\n",
    "The following paper guided us to write this notebook:\n",
    "\n",
    "* An Online Plug-and-Play Algorithm for Regularized Image Reconstruction\n",
    "https://arxiv.org/abs/1809.04693\n",
    "\n",
    "* The Little Engine that Could Regularization by Denoising (RED)\n",
    "https://arxiv.org/abs/1611.02862\n",
    "\n",
    "* Regularization by Denoising: Clarifications and New Interpretations\n",
    "https://arxiv.org/abs/1806.02296\n",
    "\n",
    "* DeepRED: Deep Image Prior Powered by RED\n",
    "https://arxiv.org/abs/1903.10176\n",
    "\n",
    "* Infusing Learned Priors into Model-Based Multispectral Imaging\n",
    "https://arxiv.org/abs/1909.09313\n",
    "\n",
    "* SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors\n",
    "https://ieeexplore.ieee.org/document/9107406\n",
    "\n",
    "* Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition\n",
    "https://arxiv.org/abs/2106.03668\n",
    "\n",
    "* Deep inverse\n",
    "https://github.com/deepinv/deepinv"
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