diff --git a/src/methods/chaari_mohamed/fonctions.ipynb b/src/methods/chaari_mohamed/fonctions.ipynb
deleted file mode 100644
index c94e976282662746f97d5eed762f16acbf3c824b..0000000000000000000000000000000000000000
--- a/src/methods/chaari_mohamed/fonctions.ipynb
+++ /dev/null
@@ -1,142 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "ec6da321",
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from scipy.signal import convolve2d\n",
- "from src.forward_model import CFA\n",
- "\n",
- "def bilinear_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:\n",
- " \"\"\"\n",
- " Bilinear demosaicing method.\n",
- "\n",
- " Args:\n",
- " op (CFA): CFA operator.\n",
- " y (np.ndarray): Mosaicked image.\n",
- "\n",
- " Returns:\n",
- " np.ndarray: Demosaicked image.\n",
- " \"\"\"\n",
- " # Copie des valeurs directement connues pour chaque canal\n",
- " red = y[:, :, 0]\n",
- " green = y[:, :, 1]\n",
- " blue = y[:, :, 2]\n",
- "\n",
- " # Création des masques pour chaque couleur selon le motif CFA\n",
- " mask_red = (op.mask == 0) # Supposons que 0 correspond au rouge dans le masque\n",
- " mask_green = (op.mask == 1) # Supposons que 1 correspond au vert\n",
- " mask_blue = (op.mask == 2) # Supposons que 2 correspond au bleu\n",
- "\n",
- " # Interpolation bilinéaire pour le rouge et le bleu\n",
- " # Note: np.multiply multiplie les éléments correspondants des tableaux, c'est pourquoi nous utilisons np.multiply au lieu de *\n",
- " red_interp = convolve2d(np.multiply(red, mask_red), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
- " blue_interp = convolve2d(np.multiply(blue, mask_blue), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
- "\n",
- " # Interpolation bilinéaire pour le vert\n",
- " # Pour le vert, nous utilisons un autre noyau car il y a plus de pixels verts\n",
- " green_interp = convolve2d(np.multiply(green, mask_green), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')\n",
- "\n",
- " # Création de l'image interpolée\n",
- " demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)\n",
- "\n",
- " # Correction des valeurs interpolées: on réapplique les valeurs connues pour éviter le flou\n",
- " demosaicked_image[:, :, 0][mask_red] = red[mask_red]\n",
- " demosaicked_image[:, :, 1][mask_green] = green[mask_green]\n",
- " demosaicked_image[:, :, 2][mask_blue] = blue[mask_blue]\n",
- "\n",
- " # Clip pour s'assurer que toutes les valeurs sont dans la plage [0, 1]\n",
- " demosaicked_image = np.clip(demosaicked_image, 0, 1)\n",
- "\n",
- " return demosaicked_image\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "id": "cf379598",
- "metadata": {},
- "outputs": [],
- "source": [
- "def quad_bayer_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:\n",
- " \"\"\"\n",
- " Demosaicing method for Quad Bayer CFA pattern.\n",
- "\n",
- " Args:\n",
- " op (CFA): CFA operator.\n",
- " y (np.ndarray): Mosaicked image.\n",
- "\n",
- " Returns:\n",
- " np.ndarray: Demosaicked image.\n",
- " \"\"\"\n",
- " \n",
- " # Interpolation bilinéaire pour chaque canal\n",
- " red_interp = convolve2d(np.multiply(y[:, :, 0], op.mask == 0), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
- " green_interp = convolve2d(np.multiply(y[:, :, 1], op.mask == 1), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')\n",
- " blue_interp = convolve2d(np.multiply(y[:, :, 2], op.mask == 2), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')\n",
- "\n",
- " # Assemblage de l'image interpolée\n",
- " demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)\n",
- "\n",
- " # Réapplication des valeurs connues\n",
- " demosaicked_image[:, :, 0][op.mask == 0] = y[:, :, 0][op.mask == 0]\n",
- " demosaicked_image[:, :, 1][op.mask == 1] = y[:, :, 1][op.mask == 1]\n",
- " demosaicked_image[:, :, 2][op.mask == 2] = y[:, :, 2][op.mask == 2]\n",
- "\n",
- " # Clip des valeurs pour les maintenir dans la plage appropriée\n",
- " demosaicked_image = np.clip(demosaicked_image, 0, 1)\n",
- "\n",
- " return demosaicked_image\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "id": "3eec062c",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a6630396",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ef4e59c8",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "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": "3.9.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/methods/chaari_mohamed/fonctions.py b/src/methods/chaari_mohamed/fonctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..4529c7a934edde76820dedd4ef0331ee77223f99
--- /dev/null
+++ b/src/methods/chaari_mohamed/fonctions.py
@@ -0,0 +1,77 @@
+import numpy as np
+from scipy.signal import convolve2d
+from src.forward_model import CFA
+
+def bilinear_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:
+ """
+ Bilinear demosaicing method.
+
+ Args:
+ op (CFA): CFA operator.
+ y (np.ndarray): Mosaicked image.
+
+ Returns:
+ np.ndarray: Demosaicked image.
+ """
+ # Copie des valeurs directement connues pour chaque canal
+ red = y[:, :, 0]
+ green = y[:, :, 1]
+ blue = y[:, :, 2]
+
+ # Création des masques pour chaque couleur selon le motif CFA
+ mask_red = (op.mask == 0) # Supposons que 0 correspond au rouge dans le masque
+ mask_green = (op.mask == 1) # Supposons que 1 correspond au vert
+ mask_blue = (op.mask == 2) # Supposons que 2 correspond au bleu
+
+ # Interpolation bilinéaire pour le rouge et le bleu
+ # Note: np.multiply multiplie les éléments correspondants des tableaux, c'est pourquoi nous utilisons np.multiply au lieu de *
+ red_interp = convolve2d(np.multiply(red, mask_red), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+ blue_interp = convolve2d(np.multiply(blue, mask_blue), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+
+ # Interpolation bilinéaire pour le vert
+ # Pour le vert, nous utilisons un autre noyau car il y a plus de pixels verts
+ green_interp = convolve2d(np.multiply(green, mask_green), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')
+
+ # Création de l'image interpolée
+ demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)
+
+ # Correction des valeurs interpolées: on réapplique les valeurs connues pour éviter le flou
+ demosaicked_image[:, :, 0][mask_red] = red[mask_red]
+ demosaicked_image[:, :, 1][mask_green] = green[mask_green]
+ demosaicked_image[:, :, 2][mask_blue] = blue[mask_blue]
+
+ # Clip pour s'assurer que toutes les valeurs sont dans la plage [0, 1]
+ demosaicked_image = np.clip(demosaicked_image, 0, 1)
+
+ return demosaicked_image
+
+
+def quad_bayer_demosaicing(op: CFA, y: np.ndarray) -> np.ndarray:
+ """
+ Demosaicing method for Quad Bayer CFA pattern.
+
+ Args:
+ op (CFA): CFA operator.
+ y (np.ndarray): Mosaicked image.
+
+ Returns:
+ np.ndarray: Demosaicked image.
+ """
+
+ # Interpolation bilinéaire pour chaque canal
+ red_interp = convolve2d(np.multiply(y[:, :, 0], op.mask == 0), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+ green_interp = convolve2d(np.multiply(y[:, :, 1], op.mask == 1), [[0, 1/4, 0], [1/4, 1, 1/4], [0, 1/4, 0]], mode='same')
+ blue_interp = convolve2d(np.multiply(y[:, :, 2], op.mask == 2), [[1/4, 1/2, 1/4], [1/2, 1, 1/2], [1/4, 1/2, 1/4]], mode='same')
+
+ # Assemblage de l'image interpolée
+ demosaicked_image = np.stack((red_interp, green_interp, blue_interp), axis=-1)
+
+ # Réapplication des valeurs connues
+ demosaicked_image[:, :, 0][op.mask == 0] = y[:, :, 0][op.mask == 0]
+ demosaicked_image[:, :, 1][op.mask == 1] = y[:, :, 1][op.mask == 1]
+ demosaicked_image[:, :, 2][op.mask == 2] = y[:, :, 2][op.mask == 2]
+
+ # Clip des valeurs pour les maintenir dans la plage appropriée
+ demosaicked_image = np.clip(demosaicked_image, 0, 1)
+
+ return demosaicked_image