{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = [cv2.imread(f'samples/series/{i}.png') for i in range(1, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gray = [cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) for im in imgs]\n",
    "gray = [np.float64(im) for im in gray]\n",
    "noise = np.random.randn(*gray[1].shape)*10\n",
    "noisy = [im + noise for im in gray]\n",
    "noisy = [np.uint8(np.clip(im, 0, 255)) for im in noisy]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dst = cv2.fastNlMeansDenoisingMulti(noisy, 2, 5, None, 4, 7, 35)"
   ]
  }
 ],
 "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": "3.7.1"
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}