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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Models Genesis in Diffusion MRI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is an example on how to implement the transforms proposed in Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysi (Zhou et al., https://arxiv.org/abs/1908.06912). \n",
+    "The transforms here follow the propositions of Models Genesis in general, but are implemented in a more modular way - you can choose which of the transforms to use, and which not.\n",
+    "There is not guarantee for correctness and/or exact reimplementation of the original transforms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import dipy, dipy.data\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Download exemplary diffusion data from dipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "hardi_fname, hardi_bval_fname, hardi_bvec_fname = dipy.data.get_fnames('stanford_hardi')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A very simple dataloader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This dataloader will return 50 3D patches (48x48x48) with 150 feature maps. This data will be used to showcase the Models Genesis transforms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rising.loading import Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class StanfordHardiDataset(Dataset):\n",
+    "    def __init__(self, hardi_fname, hardi_bval_fname, hardi_bvec_fname):\n",
+    "        data, affine = dipy.io.image.load_nifti(hardi_fname)\n",
+    "\n",
+    "        bvals, bvecs = dipy.io.gradients.read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)\n",
+    "        gtab = dipy.core.gradients.gradient_table(bvals, bvecs)\n",
+    "        \n",
+    "        mean_b0 = data[...,gtab.bvals == 0].mean(axis=-1, keepdims=True)\n",
+    "        with np.errstate(divide='ignore', invalid='ignore'):\n",
+    "            edw = np.divide(data, mean_b0)\n",
+    "        edw[~np.isfinite(edw)] = 0\n",
+    "        edw = np.clip(edw,0,1)\n",
+    "        edw = edw[...,gtab.bvals!=0]\n",
+    "        \n",
+    "        self.patches = self.random_patches(edw)\n",
+    "        \n",
+    "    def __len__(self):\n",
+    "        return len(self.patches)\n",
+    "    \n",
+    "    def __getitem__(self, idx):\n",
+    "        return (self.patches[idx], self.patches[idx])\n",
+    "    \n",
+    "    def random_patches(self, edw, n_patches=50):\n",
+    "        s = edw.shape[:3]\n",
+    "        patch_size = 48\n",
+    "        ps2 = np.int(patch_size / 2)\n",
+    "\n",
+    "        torch_patches = []\n",
+    "\n",
+    "        for n in range(n_patches):\n",
+    "            x = random.randint(ps2,s[0]-ps2)\n",
+    "            y = random.randint(ps2,s[1]-ps2)\n",
+    "            z = random.randint(ps2,s[2]-ps2)\n",
+    "\n",
+    "            patch = edw[x-ps2:x+ps2,y-ps2:y+ps2,z-ps2:z+ps2,:]\n",
+    "            patch = np.moveaxis(patch, 3, 0)\n",
+    "            patch = patch\n",
+    "\n",
+    "            torch_patches.append(torch.from_numpy(patch).type(torch.FloatTensor))\n",
+    "\n",
+    "        return torch_patches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Models Genesis Transformations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rising.loading import DataLoader, default_transform_call\n",
+    "import rising.transforms as rtr\n",
+    "from rising.random import DiscreteCombinationsParameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dMRIdataset = StanfordHardiDataset(hardi_fname, hardi_bval_fname, hardi_bvec_fname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*dMRIdataset* returns a sequence, containing two times the same 3D patch. One of those will be distorted, the other one will be left pristine, so that the final network will be able to learn the mapping from the distorted to the original patch. The different transformations are independently usable, you should choose those that are sensible for your project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: for the RandomBezierTransform, you will need to install https://github.com/aliutkus/torchsearchsorted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This function will later be used to visualize the results of the transforms\n",
+    "def visualize_pair(pair):\n",
+    "    plt.rcParams['figure.figsize'] = [10,15]\n",
+    "    img_d = pair['distorted']\n",
+    "    img_p = pair['pristine']\n",
+    "    s = img_p.shape\n",
+    "    x,y,z,n = random.randint(0,s[2]-1),random.randint(0,s[3]-1), \\\n",
+    "                random.randint(0,s[4]-1),random.randint(0,s[2]-1)\n",
+    "    x,y,z = 24,24,24\n",
+    "    plt.figure()\n",
+    "    kwargs = {\"cmap\": 'gray', \"vmin\": 0, \"vmax\": 1}\n",
+    "    plt.subplot(3, 3, 1)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.title(\"Pristine\", fontsize=20)\n",
+    "    plt.imshow(img_p[0, n, x, :, :], **kwargs)\n",
+    "    plt.subplot(3, 3, 2)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.title(\"Distorted\", fontsize=20)\n",
+    "    plt.imshow(img_d[0, n, x, :, :], **kwargs)\n",
+    "    plt.subplot(3, 3, 3)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.title(\"Difference\", fontsize=20)\n",
+    "    plt.imshow(img_d[0, n, x, :, :] - img_p[0, n, x, :, :] + 0.5, **kwargs)\n",
+    "    \n",
+    "    plt.subplot(3, 3, 4)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img_p[0, n, :, y, :], **kwargs)\n",
+    "    plt.subplot(3, 3, 5)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img_d[0, n, :, y, :], **kwargs)\n",
+    "    plt.subplot(3, 3, 6)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img_d[0, n, :, y, :] - img_p[0, n, :, y, :] + 0.5, **kwargs)\n",
+    "    \n",
+    "    plt.subplot(3, 3, 7)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img_p[0, n, :, :, z], **kwargs)\n",
+    "    plt.subplot(3, 3, 8)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img_d[0, n, :, :, z], **kwargs)\n",
+    "    plt.subplot(3, 3, 9)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(img_d[0, n, :, :, z] - img_p[0, n, :, :, z] + 0.5, **kwargs)\n",
+    "    \n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-linear transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transforms = [\n",
+    "    rtr.SeqToMap(\"distorted\", \"pristine\"),\n",
+    "    rtr.intensity.RandomBezierTransform(keys=(\"distorted\",)),\n",
+    "             ]\n",
+    "\n",
+    "dl_train = DataLoader(dMRIdataset, batch_size=3, \n",
+    "                      batch_transforms=rtr.Compose(transforms, transform_call=default_transform_call))\n",
+    "\n",
+    "visualize_pair(next(iter(dl_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Non-linear transform with inversion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transforms = [\n",
+    "    rtr.SeqToMap(\"distorted\", \"pristine\"),\n",
+    "    rtr.intensity.RandomBezierTransform(keys=(\"distorted\",)),\n",
+    "    rtr.intensity.InvertAmplitude(prob=1.0, keys=(\"distorted\",)),\n",
+    "             ]\n",
+    "\n",
+    "dl_train = DataLoader(dMRIdataset, batch_size=3, \n",
+    "                      batch_transforms=rtr.Compose(transforms, transform_call=default_transform_call))\n",
+    "\n",
+    "visualize_pair(next(iter(dl_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random pixel shuffle transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transforms = [\n",
+    "    rtr.SeqToMap(\"distorted\", \"pristine\"),\n",
+    "    rtr.painting.LocalPixelShuffle(n=100000, keys=(\"distorted\",)),\n",
+    "             ]\n",
+    "\n",
+    "dl_train = DataLoader(dMRIdataset, batch_size=3, \n",
+    "                      batch_transforms=rtr.Compose(transforms, transform_call=default_transform_call))\n",
+    "\n",
+    "visualize_pair(next(iter(dl_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Outpainting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transforms = [\n",
+    "    rtr.SeqToMap(\"distorted\", \"pristine\"),\n",
+    "    rtr.painting.RandomOutpainting(prob=1.0, keys=(\"distorted\",)),\n",
+    "             ]\n",
+    "\n",
+    "dl_train = DataLoader(dMRIdataset, batch_size=3, \n",
+    "                      batch_transforms=rtr.Compose(transforms, transform_call=default_transform_call))\n",
+    "\n",
+    "visualize_pair(next(iter(dl_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inpainting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transforms = [\n",
+    "    rtr.SeqToMap(\"distorted\", \"pristine\"),\n",
+    "    rtr.painting.RandomInpainting(n=50, keys=(\"distorted\",)),\n",
+    "             ]\n",
+    "\n",
+    "dl_train = DataLoader(dMRIdataset, batch_size=3, \n",
+    "                      batch_transforms=rtr.Compose(transforms, transform_call=default_transform_call))\n",
+    "\n",
+    "visualize_pair(next(iter(dl_train)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## All put together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x1080 with 9 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transforms = [\n",
+    "    rtr.SeqToMap(\"distorted\", \"pristine\"),\n",
+    "    rtr.spatial.Mirror(dims=(1,), prob=0.5, keys=(\"distorted\", \"pristine\")),\n",
+    "    rtr.intensity.RandomBezierTransform(keys=(\"distorted\",)),\n",
+    "    rtr.intensity.InvertAmplitude(prob=0.1, keys=(\"distorted\",)),\n",
+    "    rtr.painting.RandomInOrOutpainting(prob=0.5, n=20, keys=(\"distorted\",)),\n",
+    "             ]\n",
+    "\n",
+    "dl_train = DataLoader(dMRIdataset, batch_size=3, \n",
+    "                      batch_transforms=rtr.Compose(transforms, transform_call=default_transform_call))\n",
+    "\n",
+    "visualize_pair(next(iter(dl_train)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/rising/transforms/__init__.py b/rising/transforms/__init__.py
index f84674f2..d41e6415 100644
--- a/rising/transforms/__init__.py
+++ b/rising/transforms/__init__.py
@@ -16,6 +16,7 @@
 * Spatial Transforms
 * Tensor Transforms
 * Utility Transforms
+* Painting Transforms
 """
 
 from rising.transforms.abstract import *
@@ -29,3 +30,4 @@
 from rising.transforms.utility import *
 from rising.transforms.tensor import *
 from rising.transforms.affine import *
+from rising.transforms.painting import *
diff --git a/rising/transforms/functional/__init__.py b/rising/transforms/functional/__init__.py
index e7db8a04..91cdd11d 100644
--- a/rising/transforms/functional/__init__.py
+++ b/rising/transforms/functional/__init__.py
@@ -20,3 +20,4 @@
 from rising.transforms.functional.tensor import *
 from rising.transforms.functional.utility import *
 from rising.transforms.functional.channel import *
+from rising.transforms.functional.painting import *
diff --git a/rising/transforms/functional/intensity.py b/rising/transforms/functional/intensity.py
index 456456c2..fe490b06 100644
--- a/rising/transforms/functional/intensity.py
+++ b/rising/transforms/functional/intensity.py
@@ -3,9 +3,11 @@
 from typing import Union, Sequence, Optional
 
 from rising.utils import check_scalar
+from rising.utils.torchinterp1d import Interp1d
 
 __all__ = ["norm_range", "norm_min_max", "norm_zero_mean_unit_std", "norm_mean_std",
-           "add_noise", "add_value", "gamma_correction", "scale_by_value", "clamp"]
+           "add_noise", "add_value", "gamma_correction", "scale_by_value", "clamp",
+           "bezier_3rd_order", "random_inversion"]
 
 
 def clamp(data: torch.Tensor, min: float, max: float,
@@ -227,3 +229,39 @@ def scale_by_value(data: torch.Tensor, value: float,
         torch.Tensor: augmented data
     """
     return torch.mul(data, value, out=out)
+
+
+def bezier_3rd_order(data: torch.Tensor, maxv: float=1.0, minv: float=0.0,
+                     out: Optional[torch.Tensor] = None) -> torch.Tensor:
+    p0 = torch.zeros((1,2))
+    p1 = torch.rand((1,2))
+    p2 = torch.rand((1,2))
+    p3 = torch.ones((1,2))
+
+    t = torch.linspace(0.0, 1.0, 1000).unsqueeze(1)
+
+    points = (1-t*t*t)*p0 + 3*(1-t)*(1-t)*t*p1 + 3*(1-t)*t*t*p2 + t*t*t*p3
+
+    # scaling according to maxv,minv
+    points = points*(maxv-minv) + minv
+
+    xvals = points[:,0]
+    yvals = points[:,1]
+
+    out_flat = Interp1d()(xvals, yvals, data.view(-1))
+
+    return out_flat.view(data.shape)
+
+
+def random_inversion(data: torch.Tensor, prob_inversion: float=0.5,
+                     maxv: float=1.0, minv: float=0.0,
+                     out: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+    if torch.rand((1)) < prob_inversion:
+        # Inversion of curve
+        out = maxv + minv - data
+    else:
+        # do nothing
+        out = data
+
+    return out
diff --git a/rising/transforms/functional/painting.py b/rising/transforms/functional/painting.py
new file mode 100644
index 00000000..a57ab139
--- /dev/null
+++ b/rising/transforms/functional/painting.py
@@ -0,0 +1,118 @@
+import torch
+
+__all__ = ["local_pixel_shuffle", "random_inpainting", "random_outpainting"]
+
+
+def local_pixel_shuffle(data: torch.Tensor, n: int = -1, block_size: tuple = (0, 0, 0), rel_block_size: float = 0.1) -> torch.Tensor:
+
+    batch_size, channels, img_rows, img_cols, img_deps = data.size()
+
+    if n < 0:
+        n = int(1000 * channels)  # changes ~ 12.5% of voxels
+    for b in range(batch_size):
+        for _ in range(n):
+            c = torch.randint(0, max(1, channels - 1), (1,))
+
+            (block_size_x, block_size_y, block_size_z) = (torch.tensor([size]) for size in block_size)
+
+            if rel_block_size > 0:
+                block_size_x = torch.randint(2, max(2, int(img_rows * rel_block_size)) + 1, (1,))
+                block_size_y = torch.randint(2, max(2, int(img_cols * rel_block_size)) + 1, (1,))
+                block_size_z = torch.randint(2, max(2, int(img_deps * rel_block_size)) + 1, (1,))
+
+            x = torch.randint(0, int(img_rows - block_size_x), (1,))
+            y = torch.randint(0, int(img_cols - block_size_y), (1,))
+            z = torch.randint(0, int(img_deps - block_size_z), (1,))
+
+            window = data[b, c, x:x + block_size_x,
+                          y:y + block_size_y,
+                          z:z + block_size_z,
+                          ]
+            idx = torch.randperm(window.numel())
+            window = window.view(-1)[idx].view(window.size())
+
+            data[b, c, x:x + block_size_x,
+                 y:y + block_size_y,
+                 z:z + block_size_z] = window
+
+    return data
+
+
+def random_inpainting(data: torch.Tensor, n: int = 5, maxv: float = 1.0, minv: float = 0.0, max_size: tuple = (0, 0, 0), min_size: tuple = (0, 0, 0), rel_max_size: tuple = (0.25, 0.25, 0.25), rel_min_size: tuple = (0.1, 0.1, 0.1), min_border_distance: tuple = (3, 3, 3)) -> torch.Tensor:
+
+    batch_size, channels, img_rows, img_cols, img_deps = data.size()  # N,C,Z,X,Y
+
+    if all((rel_max >= rel_min > 0 for rel_min, rel_max in zip(rel_min_size, rel_max_size))):
+        min_x = int(rel_min_size[0] * img_rows)
+        max_x = min(img_rows - 2 * min_border_distance[0] - 1, int(rel_max_size[0] * img_rows))
+        min_y = int(rel_min_size[1] * img_cols)
+        max_y = min(img_cols - 2 * min_border_distance[1] - 1, int(rel_max_size[1] * img_cols))
+        min_z = int(rel_min_size[2] * img_deps)
+        max_z = min(img_deps - 2 * min_border_distance[2] - 1, int(rel_max_size[2] * img_deps))
+    elif all((max >= min > 0 for min, max in zip(min_size, max_size))):
+        min_x, max_x = min_size[0], max_size[0]
+        min_y, max_y = min_size[1], max_size[1]
+        min_z, max_z = min_size[2], max_size[2]
+    else:
+        raise ValueError(
+            f'random_inpainting was called with neither a valid absolut nor a valid relative min/max patch size combination. Received absolut min_size {min_size}, max_size {max_size}, and relative rel_min_size {rel_min_size}, rel_max_size {rel_max_size}')
+
+    while n > 0 and torch.rand((1)) < 0.95:
+        for b in range(batch_size):
+            block_size_x = torch.randint(min_x, max_x + 1, (1,))
+            block_size_y = torch.randint(min_y, max_y + 1, (1,))
+            block_size_z = torch.randint(min_z, max_z + 1, (1,))
+            x = torch.randint(min_border_distance[0], int(img_rows - block_size_x - min_border_distance[0]), (1,))
+            y = torch.randint(min_border_distance[1], int(img_cols - block_size_y - min_border_distance[1]), (1,))
+            z = torch.randint(min_border_distance[2], int(img_deps - block_size_z - min_border_distance[2]), (1,))
+
+            block = torch.rand((1, channels, block_size_x, block_size_y, block_size_z)) \
+                * (maxv - minv) + minv
+
+            data[b, :, x:x + block_size_x,
+                 y:y + block_size_y,
+                 z:z + block_size_z] = block
+
+            n = n - 1
+
+    return data
+
+
+def random_outpainting(data: torch.Tensor, maxv: float = 1.0, minv: float = 0.0, max_size: tuple = (0, 0, 0), min_size: tuple = (0, 0, 0), rel_max_size: tuple = (6 / 7, 6 / 7, 6 / 7), rel_min_size: tuple = (5 / 7, 5 / 7, 5 / 7), min_border_distance=(3, 3, 3)) -> torch.Tensor:
+
+    batch_size, channels, img_rows, img_cols, img_deps = data.size()
+
+    if all((rel_max >= rel_min > 0 for rel_min, rel_max in zip(rel_min_size, rel_max_size))):
+        min_x = int(rel_min_size[0] * img_rows)
+        # min() is necessary to have guarantee y > x for torch.randint(x,y) calls
+        # lowest possible index for block start is min_border_distance[i], highest possible is img_rows - block_size - min_border_distance[i]. -> block_size < img_rows - 2 * min_border_distance
+        max_x = min(img_rows - 2 * min_border_distance[0] - 1, int(rel_max_size[0] * img_rows))
+        min_y = int(rel_min_size[1] * img_cols)
+        max_y = min(img_cols - 2 * min_border_distance[1] - 1, int(rel_max_size[1] * img_cols))
+        min_z = int(rel_min_size[2] * img_deps)
+        max_z = min(img_deps - 2 * min_border_distance[2] - 1, int(rel_max_size[2] * img_deps))
+
+    elif all((max >= min > 0 for min, max in zip(min_size, max_size))):
+        min_x, max_x = min_size[0], max_size[0]
+        min_y, max_y = min_size[1], max_size[1]
+        min_z, max_z = min_size[2], max_size[2]
+    else:
+        raise ValueError(
+            f'random_inpainting was called with neither a valid absolut nor a valid relative min/max patch size combination. Received absolut min_size {min_size}, max_size {max_size}, and relative rel_min_size {rel_min_size}, rel_max_size {rel_max_size}')
+
+    out = torch.rand(data.size()) * (maxv - minv) + minv
+
+    block_size_x = torch.randint(min_x, max_x + 1, (1,))
+    block_size_y = torch.randint(min_y, max_y + 1, (1,))
+    block_size_z = torch.randint(min_z, max_z + 1, (1,))
+    x = torch.randint(min_border_distance[0], int(img_rows - block_size_x - min_border_distance[0]), (1,))
+    y = torch.randint(min_border_distance[1], int(img_cols - block_size_y - min_border_distance[1]), (1,))
+    z = torch.randint(min_border_distance[2], int(img_deps - block_size_z - min_border_distance[2]), (1,))
+
+    out[:, :, x:x + block_size_x,
+        y:y + block_size_y,
+        z:z + block_size_z] = data[:, :, x:x + block_size_x,
+                                   y:y + block_size_y,
+                                   z:z + block_size_z]
+
+    return out
diff --git a/rising/transforms/functional/spatial.py b/rising/transforms/functional/spatial.py
index 5ee3d7b1..aa0be820 100644
--- a/rising/transforms/functional/spatial.py
+++ b/rising/transforms/functional/spatial.py
@@ -19,7 +19,7 @@ def mirror(data: torch.Tensor, dims: Union[int, Sequence[int]]) -> torch.Tensor:
     """
     if check_scalar(dims):
         dims = (dims,)
-    # batch and channel dims
+        # batch and channel dims
     dims = [d + 2 for d in dims]
     return data.flip(dims)
 
diff --git a/rising/transforms/intensity.py b/rising/transforms/intensity.py
index d706607c..07f66dbf 100644
--- a/rising/transforms/intensity.py
+++ b/rising/transforms/intensity.py
@@ -12,13 +12,18 @@
     gamma_correction,
     add_value,
     scale_by_value,
-    clamp)
+    clamp,
+    bezier_3rd_order,
+    random_inversion,
+)
+
 from rising.random import AbstractParameter
 
 __all__ = ["Clamp", "NormRange", "NormMinMax",
            "NormZeroMeanUnitStd", "NormMeanStd", "Noise",
            "GaussianNoise", "ExponentialNoise", "GammaCorrection",
-           "RandomValuePerChannel", "RandomAddValue", "RandomScaleValue"]
+           "RandomValuePerChannel", "RandomAddValue", "RandomScaleValue",
+           "RandomBezierTransform", "InvertAmplitude"]
 
 
 class Clamp(BaseTransform):
@@ -303,3 +308,24 @@ def __init__(self, random_sampler: AbstractParameter,
         """
         super().__init__(augment_fn=scale_by_value, random_sampler=random_sampler,
                          per_channel=per_channel, keys=keys, grad=grad, **kwargs)
+
+
+class RandomBezierTransform(BaseTransform):
+    """ Apply a random 3rd order bezier spline to the intensity values,
+    as proposed in Models Genesis """
+
+    def __init__(self, maxv: float = 1.0, minv: float = 0.0, keys: Sequence = ('data',), **kwargs):
+
+        super().__init__(augment_fn=bezier_3rd_order, maxv=maxv, minv=minv, keys=keys, grad=False, **kwargs)
+
+
+class InvertAmplitude(BaseTransform):
+    """ Inverts the amplitude with probability p according to the following formula:
+        out = maxv + minv - data
+    """
+
+    def __init__(self, prob: float = 0.5, maxv: float = 1.0, minv: float = 0.0,
+                 keys: Sequence = ('data',), **kwargs):
+
+        super().__init__(augment_fn=random_inversion, prob_inversion=prob, maxv=maxv, minv=minv,
+                         keys=keys, grad=False, **kwargs)
diff --git a/rising/transforms/painting.py b/rising/transforms/painting.py
new file mode 100644
index 00000000..efb77a5f
--- /dev/null
+++ b/rising/transforms/painting.py
@@ -0,0 +1,140 @@
+import torch
+from typing import Sequence
+
+from rising.transforms.abstract import AbstractTransform, BaseTransform
+from rising.transforms.functional.painting import (
+    local_pixel_shuffle, random_inpainting, random_outpainting
+)
+
+
+__all__ = ["RandomInpainting", "RandomOutpainting", "RandomInOrOutpainting", "LocalPixelShuffle"]
+
+
+class LocalPixelShuffle(BaseTransform):
+    """ Shuffels Pixels locally in n patches,
+    as proposed in Models Genesis """
+
+    def __init__(self, n: int = -1, block_size: tuple = (0, 0, 0), rel_block_size: float = 0.1,
+                 keys: Sequence = ('data',), grad: bool = False, **kwargs):
+        """
+        Args:
+            n: number of local patches to shuffle, default = 1000*channels
+            block_size: size of local patches in pixel
+            rel_block_size: size of local patches in relation to image size, e.g. image_size=(32,192,192) and rel_block_size=0.25 will result in patches of size (8, 48, 48). If rel_block_size > 0, it will overwrite block_size.
+            keys: the keys corresponding to the values to distort
+            grad: enable gradient computation inside transformation
+            **kwargs: keyword arguments passed to augment_fn
+        """
+        super().__init__(augment_fn=local_pixel_shuffle, n=n, block_size=block_size, rel_block_size=rel_block_size,
+                         keys=keys, grad=grad, **kwargs)
+
+
+class RandomInpainting(BaseTransform):
+    """ In n local areas, the image is replaced by uniform noise in range (minv, maxv),
+    as proposed in Models Genesis """
+
+    def __init__(self, n: int = 5,
+                 maxv: float = 1.0, minv: float = 0.0,
+                 max_size: tuple = (0, 0, 0), min_size: tuple = (0, 0, 0), rel_max_size: tuple = (0.25, 0.25, 0.25), rel_min_size: tuple = (0.1, 0.1, 0.1), min_border_distance: tuple = (3, 3, 3),
+                 keys: Sequence = ('data',), grad: bool = False, **kwargs):
+        """
+        Args:
+            minv, maxv: range of uniform noise
+            n: number of local patches to randomize
+            max_size: absolute upper bound for the patch size 
+            min_size: absolute lower bound for the patch size
+            rel_max_size: relative upper bound for the patch size, relative to image_size. Overwrites max_size.
+            rel_min_size: relative lower bound for the patch size, relative to image_size. Overwrites min_size.
+            min_border_distance: the minimum distance of patches to the border in pixel for each dimension.
+            keys: the keys corresponding to the values to distort
+            grad: enable gradient computation inside transformation
+            **kwargs: keyword arguments passed to augment_fn
+        """
+        super().__init__(augment_fn=random_inpainting, n=n, maxv=maxv, minv=minv, max_size=max_size, min_size=min_size, rel_max_size=rel_max_size, rel_min_size=rel_min_size,
+                         keys=keys, grad=grad, **kwargs)
+
+
+class RandomOutpainting(AbstractTransform):
+    """ The border of the images will be replaced by uniform noise,
+    as proposed in Models Genesis. (Replaces a patch in an equally sized noise image with the corresponding input image content) """
+
+    def __init__(self, prob: float = 0.5, maxv: float = 1.0, minv: float = 0.0,
+                 max_size: tuple = (0, 0, 0), min_size: tuple = (0, 0, 0),
+                 rel_max_size: tuple = (6 / 7, 6 / 7, 6 / 7), rel_min_size: tuple = (5 / 7, 5 / 7, 5 / 7), min_border_distance: tuple = (3, 3, 3),
+                 keys: Sequence = ('data',), grad: bool = False, **kwargs):
+        """
+        Args:
+            minv, maxv: range of uniform noise
+            prob: probability of outpainting. For prob<1.0, not all images will be augmented
+            max_size: absolute upper bound for the patch size. Here the patch is the remaining image
+            min_size: absolute lower bound for the patch size. Here the patch is the remaining image
+            rel_max_size: relative upper bound for the patch size, relative to image_size. Overwrites max_size.
+            rel_min_size: relative lower bound for the patch size, relative to image_size. Overwrites min_size.
+            min_border_distance: the minimum thickness of the border in pixel for each dimension.
+            keys: the keys corresponding to the values to distort
+            grad: enable gradient computation inside transformation
+            **kwargs: keyword arguments passed to augment_fn
+        """
+        super().__init__(grad=grad, **kwargs)
+        self.prob = prob
+        self.maxv = maxv
+        self.minv = minv
+        self.keys = keys
+        self.max_size = max_size
+        self.min_size = min_size
+        self.rel_min_size = rel_min_size
+        self.rel_max_size = rel_max_size
+        self.min_border_distance = min_border_distance
+
+    def forward(self, **data) -> dict:
+        if torch.rand(1) < self.prob:
+            for key in self.keys:
+                data[key] = random_outpainting(data[key], maxv=self.maxv, minv=self.minv, max_size=self.max_size, min_size=self.min_size, rel_max_size=self.rel_max_size, rel_min_size=self.rel_min_size,
+                                               min_border_distance=self.min_border_distance)
+        return data
+
+
+class RandomInOrOutpainting(AbstractTransform):
+    """Applies either random inpainting or random outpainting to the image,
+    as proposed in Models Genesis """
+
+    def __init__(self, prob: float = 0.5, n: int = 5,
+                 maxv: float = 1.0, minv: float = 0.0,
+                 max_size_in: tuple = (0, 0, 0), min_size_in: tuple = (0, 0, 0), rel_max_size_in: tuple = (0.25, 0.25, 0.25), rel_min_size_in: tuple = (0.1, 0.1, 0.1),
+                 max_size_out: tuple = (0, 0, 0), min_size_out: tuple = (0, 0, 0),
+                 rel_max_size_out: tuple = (6 / 7, 6 / 7, 6 / 7), rel_min_size_out: tuple = (5 / 7, 5 / 7, 5 / 7),
+                 keys: Sequence = ('data',), grad: bool = False, **kwargs):
+        """
+        Args:
+            minv, maxv: range of uniform noise
+            prob: probability of outpainting, probability of inpainting is 1-prob.
+            n: number of local patches to randomize in case of inpainting
+            keys: the keys corresponding to the values to distort
+            grad: enable gradient computation inside transformation
+            **kwargs: keyword arguments passed to augment_fn
+        """
+        super().__init__(grad=grad, **kwargs)
+        self.prob = prob
+        self.maxv = maxv
+        self.minv = minv
+        self.keys = keys
+        self.n = n
+        self.max_size_in = max_size_in
+        self.min_size_in = min_size_in
+        self.rel_min_size_in = rel_min_size_in
+        self.rel_max_size_in = rel_max_size_in
+        self.max_size_out = max_size_out
+        self.min_size_out = min_size_out
+        self.rel_min_size_out = rel_min_size_out
+        self.rel_max_size_out = rel_max_size_out
+
+    def forward(self, **data) -> dict:
+        if torch.rand(1) < self.prob:
+            for key in self.keys:
+                data[key] = random_outpainting(data[key], maxv=self.maxv, minv=self.minv, max_size=self.max_size_out,
+                                               min_size=self.min_size_out, rel_max_size=self.rel_max_size_out, rel_min_size=self.rel_min_size_out)
+        else:
+            for key in self.keys:
+                data[key] = random_inpainting(data[key], n=self.n, maxv=self.maxv, minv=self.minv, max_size=self.max_size_in,
+                                              min_size=self.min_size_in, rel_max_size=self.rel_max_size_in, rel_min_size=self.rel_min_size_in)
+        return data
diff --git a/rising/transforms/spatial.py b/rising/transforms/spatial.py
index f736b1fa..a511f9e3 100644
--- a/rising/transforms/spatial.py
+++ b/rising/transforms/spatial.py
@@ -16,13 +16,12 @@
 scheduler_type = Callable[[int], Union[int, Sequence[int]]]
 
 
-class Mirror(BaseTransform):
+class Mirror(AbstractTransform):
     """Random mirror transform"""
 
-    def __init__(self,
-                 dims: Union[int, DiscreteParameter,
-                             Sequence[Union[int, DiscreteParameter]]],
-                 keys: Sequence[str] = ('data',), grad: bool = False, **kwargs):
+    def __init__(self, dims: Union[int, DiscreteParameter, Sequence[Union[int, DiscreteParameter]]],
+                 keys: Sequence[str] = ('data',), prob: float = 0.5,
+                 grad: bool = False, **kwargs):
         """
         Args:
             dims: axes which should be mirrored
@@ -39,8 +38,30 @@ def __init__(self,
             >>> # volumetric data
             >>> trafo = Mirror(DiscreteCombinationsParameter((0, 1, 2)))
         """
-        super().__init__(augment_fn=mirror, dims=dims, keys=keys, grad=grad,
-                         property_names=('dims',), **kwargs)
+        super().__init__(grad=grad, **kwargs)
+        self.keys = keys
+        self.prob = prob
+        if not isinstance(dims, DiscreteParameter):
+            if len(dims) > 2:
+                dims = list(combinations(dims, 2))
+            else:
+                dims = (dims,)
+            dims = DiscreteParameter(dims)
+        self.register_sampler("dims", dims)
+
+    def forward(self, **data) -> dict:
+        """
+        Apply transformation
+
+        Args:
+            data: dict with tensors
+        Returns:
+            dict: dict with augmented data
+        """
+        if torch.rand(1) < self.prob:
+            for key in self.keys:
+                data[key] = mirror(data[key], self.dims)
+        return data
 
 
 class Rot90(AbstractTransform):
diff --git a/rising/utils/torchinterp1d.py b/rising/utils/torchinterp1d.py
new file mode 100644
index 00000000..b3219290
--- /dev/null
+++ b/rising/utils/torchinterp1d.py
@@ -0,0 +1,204 @@
+"""
+Code taken from: https://github.com/aliutkus/torchinterp1d
+BSD 3-Clause License
+
+Copyright (c) 2019, Inria (Antoine Liutkus)
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import torch
+import contextlib
+SEARCHSORTED_AVAILABLE = True
+try:
+    from torchsearchsorted import searchsorted
+except ImportError:
+    SEARCHSORTED_AVAILABLE = False
+
+
+class Interp1d(torch.autograd.Function):
+    def __call__(self, x, y, xnew, out=None):
+        return self.forward(x, y, xnew, out)
+
+    def forward(ctx, x, y, xnew, out=None):
+        """
+        Linear 1D interpolation on the GPU for Pytorch.
+        This function returns interpolated values of a set of 1-D functions at
+        the desired query points `xnew`.
+        This function is working similarly to Matlab™ or scipy functions with
+        the `linear` interpolation mode on, except that it parallelises over
+        any number of desired interpolation problems.
+        The code will run on GPU if all the tensors provided are on a cuda
+        device.
+
+        Parameters
+        ----------
+        x : (N, ) or (D, N) Pytorch Tensor
+            A 1-D or 2-D tensor of real values.
+        y : (N,) or (D, N) Pytorch Tensor
+            A 1-D or 2-D tensor of real values. The length of `y` along its
+            last dimension must be the same as that of `x`
+        xnew : (P,) or (D, P) Pytorch Tensor
+            A 1-D or 2-D tensor of real values. `xnew` can only be 1-D if
+            _both_ `x` and `y` are 1-D. Otherwise, its length along the first
+            dimension must be the same as that of whichever `x` and `y` is 2-D.
+        out : Pytorch Tensor, same shape as `xnew`
+            Tensor for the output. If None: allocated automatically.
+
+        """
+        # checking availability of the searchsorted pytorch module
+        if not SEARCHSORTED_AVAILABLE:
+            raise Exception(
+                'The interp1d function depends on the '
+                'torchsearchsorted module, which is not available.\n'
+                'You must get it at ',
+                'https://github.com/aliutkus/torchsearchsorted \n')
+
+        # making the vectors at least 2D
+        is_flat = {}
+        require_grad = {}
+        v = {}
+        device = []
+        eps = torch.finfo(y.dtype).eps
+        for name, vec in {'x': x, 'y': y, 'xnew': xnew}.items():
+            assert len(vec.shape) <= 2, 'interp1d: all inputs must be '\
+                                        'at most 2-D.'
+            if len(vec.shape) == 1:
+                v[name] = vec[None, :]
+            else:
+                v[name] = vec
+            is_flat[name] = v[name].shape[0] == 1
+            require_grad[name] = vec.requires_grad
+            device = list(set(device + [str(vec.device)]))
+        assert len(device) == 1, 'All parameters must be on the same device.'
+        device = device[0]
+
+        # Checking for the dimensions
+        assert (v['x'].shape[1] == v['y'].shape[1]
+                and (
+                     v['x'].shape[0] == v['y'].shape[0]
+                     or v['x'].shape[0] == 1
+                     or v['y'].shape[0] == 1
+                    )
+                ), ("x and y must have the same number of columns, and either "
+                    "the same number of row or one of them having only one "
+                    "row.")
+
+        reshaped_xnew = False
+        if ((v['x'].shape[0] == 1) and (v['y'].shape[0] == 1)
+           and (v['xnew'].shape[0] > 1)):
+            # if there is only one row for both x and y, there is no need to
+            # loop over the rows of xnew because they will all have to face the
+            # same interpolation problem. We should just stack them together to
+            # call interp1d and put them back in place afterwards.
+            original_xnew_shape = v['xnew'].shape
+            v['xnew'] = v['xnew'].contiguous().view(1, -1)
+            reshaped_xnew = True
+
+        # identify the dimensions of output and check if the one provided is ok
+        D = max(v['x'].shape[0], v['xnew'].shape[0])
+        shape_ynew = (D, v['xnew'].shape[-1])
+        if out is not None:
+            if out.numel() != shape_ynew[0]*shape_ynew[1]:
+                # The output provided is of incorrect shape.
+                # Going for a new one
+                out = None
+            else:
+                ynew = out.reshape(shape_ynew)
+        if out is None:
+            ynew = torch.zeros(*shape_ynew, device=device)
+
+        # moving everything to the desired device in case it was not there
+        # already (not handling the case things do not fit entirely, user will
+        # do it if required.)
+        for name in v:
+            v[name] = v[name].to(device)
+
+        # calling searchsorted on the x values.
+        ind = ynew.long()
+        searchsorted(v['x'].contiguous(), v['xnew'].contiguous(), ind)
+
+        # the `-1` is because searchsorted looks for the index where the values
+        # must be inserted to preserve order. And we want the index of the
+        # preceeding value.
+        ind -= 1
+        # we clamp the index, because the number of intervals is x.shape-1,
+        # and the left neighbour should hence be at most number of intervals
+        # -1, i.e. number of columns in x -2
+        ind = torch.clamp(ind, 0, v['x'].shape[1] - 1 - 1)
+
+        # helper function to select stuff according to the found indices.
+        def sel(name):
+            if is_flat[name]:
+                return v[name].contiguous().view(-1)[ind]
+            return torch.gather(v[name], 1, ind)
+
+        # activating gradient storing for everything now
+        enable_grad = False
+        saved_inputs = []
+        for name in ['x', 'y', 'xnew']:
+            if require_grad[name]:
+                enable_grad = True
+                saved_inputs += [v[name]]
+            else:
+                saved_inputs += [None, ]
+        # assuming x are sorted in the dimension 1, computing the slopes for
+        # the segments
+        is_flat['slopes'] = is_flat['x']
+        # now we have found the indices of the neighbors, we start building the
+        # output. Hence, we start also activating gradient tracking
+        with torch.enable_grad() if enable_grad else contextlib.suppress():
+            v['slopes'] = (
+                    (v['y'][:, 1:]-v['y'][:, :-1])
+                    /
+                    (eps + v['x'][:, 1:]-v['x'][:, :-1])
+                )
+
+            # now build the linear interpolation
+            ynew = sel('y') + sel('slopes')*(
+                                    v['xnew'] - sel('x'))
+
+            if reshaped_xnew:
+                ynew = ynew.view(original_xnew_shape)
+
+        ctx.save_for_backward(ynew, *saved_inputs)
+        return ynew
+
+    @staticmethod
+    def backward(ctx, grad_out):
+        inputs = ctx.saved_tensors[1:]
+        gradients = torch.autograd.grad(
+                        ctx.saved_tensors[0],
+                        [i for i in inputs if i is not None],
+                        grad_out, retain_graph=True)
+        result = [None, ] * 5
+        pos = 0
+        for index in range(len(inputs)):
+            if inputs[index] is not None:
+                result[index] = gradients[pos]
+                pos += 1
+        return (*result,)
\ No newline at end of file