diff --git a/src/methods/dzik_erwan/__init__.py b/src/methods/dzik_erwan/__init__.py
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diff --git a/src/methods/dzik_erwan/dzik_erwan.pdf b/src/methods/dzik_erwan/dzik_erwan.pdf
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diff --git a/src/methods/dzik_erwan/functions.py b/src/methods/dzik_erwan/functions.py
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+++ b/src/methods/dzik_erwan/functions.py
@@ -0,0 +1,115 @@
+import numpy as np
+from scipy.ndimage import convolve
+
+from src.forward_model import CFA
+
+def malvar2004(y,op):
+ "Code inspiered by KelSolaar's GitHub repo:"
+ "https://github.com/colour-science/colour-demosaicing/tree/master"
+
+ z = op.adjoint(y)
+ mask = op.mask
+
+ R = z[:,:,0]
+ G = z[:,:,1]
+ B = z[:,:,2]
+
+ R_m, G_m, B_m = mask[:,:,0], mask[:,:,1], mask[:,:,2]
+
+
+ G = np.where(np.logical_or(R_m == 1, B_m == 1), convolve(y, GR_GB), G)
+
+
+ #Logical array that verify the conditon of the different filters
+ # Red rows.
+ R_r = np.transpose(np.any(R_m == 1, axis=1)[None]) * np.ones(R.shape)
+ # Red columns.
+ R_c = np.any(R_m == 1, axis=0)[None] * np.ones(R.shape)
+ # Blue rows.
+ B_r = np.transpose(np.any(B_m == 1, axis=1)[None]) * np.ones(B.shape)
+ # Blue columns
+ B_c = np.any(B_m == 1, axis=0)[None] * np.ones(B.shape)
+
+ #Results of the convolutions
+ RBg_RBBR = convolve(y, Rg_RB_Bg_BR)
+ RBg_BRRB = convolve(y, Rg_BR_Bg_RB)
+ RBgr_BBRR = convolve(y, Rb_BB_Br_RR)
+
+
+ R = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_RBBR, R)
+ R = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_BRRB, R)
+
+ B = np.where(np.logical_and(B_r == 1, R_c == 1), RBg_RBBR, B)
+ B = np.where(np.logical_and(R_r == 1, B_c == 1), RBg_BRRB, B)
+
+ R = np.where(np.logical_and(B_r == 1, B_c == 1), RBgr_BBRR, R)
+ B = np.where(np.logical_and(R_r == 1, R_c == 1), RBgr_BBRR, B)
+
+
+ res = np.clip(np.stack((R,G,B), axis = 2), 0, 1)
+
+
+ return res
+
+def quad_bayer_to_bayer(cfa_quad_bayer):
+ "Quad to Bayer by swapping method"
+
+ cfa_bayer = np.flip(np.copy(cfa_quad_bayer), 1)
+ h,w = cfa_bayer.shape
+ for col in range(1, w, 4):
+ temp = np.copy(cfa_bayer[:, col])
+ cfa_bayer[:, col] = cfa_bayer[:, col + 1]
+ cfa_bayer[:, col + 1] = temp
+ del temp
+
+
+ for row in range(1, h, 4):
+ temp = np.copy(cfa_bayer[row, :])
+ cfa_bayer[row, :] = cfa_bayer[row + 1, :]
+ cfa_bayer[row + 1, :] = temp
+ del temp
+
+ for row in range(1, h, 4):
+ for col in range(2, w, 4):
+ g1 = cfa_bayer[row, col]
+ cfa_bayer[row, col] = cfa_bayer[row + 1, col -1]
+ cfa_bayer[row + 1, col -1] = g1
+
+ return np.flip(cfa_bayer,1)
+
+# G at R locations or G at B locations
+GR_GB = (1/8) * np.asarray([
+ [0., 0., -1., 0., 0.],
+ [0., 0., 2., 0., 0.],
+ [-1., 2., 4., 2., -1.],
+ [0., 0., 2., 0., 0.],
+ [0., 0., -1., 0., 0.],
+])
+
+#R at green in R row, B column or B at green in B row, R colunm
+Rg_RB_Bg_BR = (1/8) * np.asarray([
+ [0., 0., .5, 0., 0.],
+ [0., -1., 0., -1., 0.],
+ [-1., 4., 5., 4., -1.],
+ [0., -1., 0., -1., 0.],
+ [0., 0., .5, 0., 0.],
+])
+
+# R at green in B row, R column or B at green in R row, B column
+Rg_BR_Bg_RB = (1/8) * np.asarray([
+ [0., 0., -1, 0., 0.],
+ [0., -1., 4., -1., 0.],
+ [.5, 0., 5., 0., .5],
+ [0., -1., 4., -1., 0.],
+ [0., 0., -1, 0., 0.],
+])
+
+#R at blue in B row, B column or B at red in R row, R column
+Rb_BB_Br_RR = (1/8) * np.asarray([
+ [0., 0., -1.5, 0., 0.],
+ [0., 2., 0., 2., 0.],
+ [-1.5, 0., 6., 0., -1.5],
+ [0., 2., 0., 2., 0.],
+ [0., 0., -1.5, 0., 0.],
+])
+
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diff --git a/src/methods/dzik_erwan/output/quad_bayer/reconstructed_img4.png b/src/methods/dzik_erwan/output/quad_bayer/reconstructed_img4.png
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diff --git a/src/methods/dzik_erwan/reconstruct.py b/src/methods/dzik_erwan/reconstruct.py
new file mode 100644
index 0000000000000000000000000000000000000000..424b01c5e921152902d9e02ecc80beb1ac4adeea
--- /dev/null
+++ b/src/methods/dzik_erwan/reconstruct.py
@@ -0,0 +1,61 @@
+"""The main file for the reconstruction.
+This file should NOT be modified except the body of the 'run_reconstruction' function.
+Students can call their functions (declared in others files of src/methods/your_name).
+"""
+
+
+import numpy as np
+
+from src.forward_model import CFA
+from src.methods.dzik_erwan.functions import malvar2004, quad_bayer_to_bayer
+
+
+
+def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
+ """Performs demosaicking on y.
+
+ Args:
+ y (np.ndarray): Mosaicked image to be reconstructed.
+ cfa (str): Name of the CFA. Can be bayer or quad_bayer.
+
+ Returns:
+ np.ndarray: Demosaicked image.
+ """
+ # Performing the reconstruction.
+ # TODO
+ input_shape = (y.shape[0], y.shape[1], 3)
+ op = CFA(cfa, input_shape)
+
+ if cfa == 'bayer':
+ res = malvar2004(y,op)
+ else:
+ bayer_equivalent = quad_bayer_to_bayer(y)
+ res = malvar2004(bayer_equivalent, CFA("bayer", input_shape))
+
+ return res
+
+
+####
+####
+####
+
+#### #### #### #############
+#### ###### #### ##################
+#### ######## #### ####################
+#### ########## #### #### ########
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+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller