diff --git a/src/methods/babacar/babacar_diop.pdf b/src/methods/babacar/babacar_diop.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..76bdc85d9fbc9965cbd73e9c90ff2f98b0c33a93
Binary files /dev/null and b/src/methods/babacar/babacar_diop.pdf differ
diff --git a/src/methods/babacar/demoisaicing_fct.py b/src/methods/babacar/demoisaicing_fct.py
new file mode 100644
index 0000000000000000000000000000000000000000..3930007e78ffd7ecc3db498dbbc53d8d19ff6306
--- /dev/null
+++ b/src/methods/babacar/demoisaicing_fct.py
@@ -0,0 +1,258 @@
+"""A file containing a (pretty useless) reconstruction.
+It serves as example of how the project works.
+This file should NOT be modified.
+"""
+
+
+import numpy as np
+from scipy.signal import convolve2d
+
+from src.forward_model import CFA
+
+
+def High_Quality_Linear_Interpolation(op: CFA, y: np.ndarray) -> np.ndarray:
+ """Performs a High-Quality Linear Interpolation of the lost pixels.
+
+ Args:
+ op (CFA): CFA operator.
+ y (np.ndarray): Mosaicked image.
+
+ Returns:
+ np.ndarray: Demosaicked image.
+ """
+ z = op.adjoint(y)
+ #operation for the bayer pattern
+ if op.cfa == 'bayer':
+ res = np.empty(op.input_shape)
+ mask = op.mask
+ R = 0
+ G = 1
+ B = 2
+
+ y_padded = np.pad(y,2, 'constant', constant_values=0)
+
+ for i in range(2,y_padded.shape[0]-2):
+ for j in range(2,y_padded.shape[1]-2):
+ for c in range(op.input_shape[2]):
+ i_real = i-2
+ j_real = j-2
+ if (c==R and mask[i_real,j_real,R] == 1) or (c==G and mask[i_real,j_real,G] == 1)or (c==B and mask[i_real,j_real,B] == 1): #no need to find the pixel value
+ res[i_real,j_real,c] = y_padded[i,j]
+ elif (c == G) and (mask[i_real,j_real,B] == 1 or mask[i_real,j_real,R] == 1): #green channel and pixel value = B or R
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_g_r_and_b, mode='valid'))
+
+ elif (c == R or c== B )and (mask[i_real,j_real,G] == 1) and (mask[i_real,j_real-1,c] == 0): #red or blue channel and pixel value = G and left pixel isn't in the recovered layer
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_r_and_b_g_brow_rcol, mode='valid'))
+
+ elif (c == R or c == B) and (mask[i_real,j_real,G] == 1) and (mask[i_real-1,j_real,c] == 0): #red or blue channel and pixel value = G and up pixel isn't in the recovered layer
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_r_and_b_g_rrow_bcol, mode='valid'))
+
+ elif (c == R and mask[i_real,j_real,B] == 1) or (c == B and mask[i_real,j_real,R] == 1) : #red channel and pixel value = B or inverse
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-2:i+3, j-2:j+3], hq_r_b, mode='valid'))
+ else : print("error")
+
+ #operation for the quad bayer pattern
+ else:
+ res = np.empty(op.input_shape)
+ mask = op.mask
+ R = 0
+ G = 1
+ B = 2
+
+ y_padded = np.pad(y,4, 'constant', constant_values=0)
+
+ for i in range(4,y_padded.shape[0]-4, 2):
+ for j in range(4,y_padded.shape[1]-4, 2):
+ for c in range(op.input_shape[2]):
+ i_real = i-4
+ j_real = j-4
+ if (c==R and mask[i_real,j_real,R] == 1) or (c==G and mask[i_real,j_real,G] == 1)or (c==B and mask[i_real,j_real,B] == 1): #no need to find the pixel value
+ res[i_real:i_real+2,j_real:j_real+2,c] = y_padded[i:i+2,j:j+2]
+
+ elif (c == G) and (mask[i_real,j_real,B] == 1 or mask[i_real,j_real,R] == 1): #green channel and pixel value = B or R
+ # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_g_r_and_b, mode='valid'))
+
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_g_r_and_b, mode='valid'))
+ res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_g_r_and_b, mode='valid'))
+ res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_g_r_and_b, mode='valid'))
+ res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_g_r_and_b, mode='valid'))
+
+ elif (c == R or c== B )and (mask[i_real,j_real,G] == 1) and (mask[i_real,j_real-2,c] == 0): #red or blue channel and pixel value = G and left pixel isn't in the recovered layer
+ # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+
+
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+ res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+ res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+ res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_r_and_b_g_brow_rcol, mode='valid'))
+
+ elif (c == R or c == B) and (mask[i_real,j_real,G] == 1) and (mask[i_real-2,j_real,c] == 0): #red or blue channel and pixel value = G and up pixel isn't in the recovered layer
+ # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+ res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+ res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+ res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_r_and_b_g_rrow_bcol, mode='valid'))
+
+
+ elif (c == R and mask[i_real,j_real,B] == 1) or (c == B and mask[i_real,j_real,R] == 1) : #red channel and pixel value = B or inverse
+ # res[i_real:i_real+2,j_real:j_real+2,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], hq_quad_r_b, mode='valid'))
+
+ res[i_real,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], A_hq_quad_r_b, mode='valid'))
+ res[i_real,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], B_hq_quad_r_b, mode='valid'))
+ res[i_real+1,j_real,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], C_hq_quad_r_b, mode='valid'))
+ res[i_real+1,j_real+1,c] = float(convolve2d(y_padded[i-4:i+6, j-4:j+6], D_hq_quad_r_b, mode='valid'))
+ else : print("error")
+ np.clip(res, 0, 1, res)
+ return res
+
+
+# Bayer masks
+
+hq_g_r_and_b = np.array([[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]]) / 8
+
+hq_r_and_b_g_rrow_bcol = np.array([[0, 0, 1/2, 0, 0], [0, -1, 0, -1, 0], [-1, 4, 5, 4, -1], [0, -1, 0, -1, 0], [0, 0, 1/2, 0, 0]]) / 8
+
+hq_r_and_b_g_brow_rcol = hq_r_and_b_g_rrow_bcol.T
+
+hq_r_b = np.array([[0, 0, -3/2, 0, 0], [0, 2, 0, 2, 0], [-3/2, 0, 6, 0, -3/2], [0, 2, 0, 2, 0], [0, 0, -3/2, 0, 0]]) / 8
+
+
+# Quad bayer masks
+
+## Calcul the value of 4 pixels with one mask, then the value is apply for the 4 pixels
+
+
+hq_quad_g_r_and_b = np.array([[0,0, 0,0, -1,-1, 0,0, 0,0],[0,0, 0,0, -1,-1, 0,0, 0,0],
+ [0,0,0, 0,2, 2, 0,0, 0,0],[0,0,0, 0,2, 2, 0,0, 0,0],
+ [-1,-1, 2,2, 4,4, 2,2, -1,-1],[-1,-1, 2,2, 4,4, 2,2, -1,-1],
+ [0,0, 0,0, 2,2, 0,0, 0,0],[0,0, 0,0, 2,2, 0,0, 0,0],
+ [0,0, 0,0, -1,-1, 0,0, 0,0],[0,0, 0,0, -1,-1, 0,0, 0,0]]) / 16
+
+hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 1/2,1/2, 0,0, 0,0],[0,0, 0,0, 1/2,1/2, 0,0, 0,0],
+ [0,0, -1,-1, 0,0, -1,-1, 0,0],[0,0, -1,-1, 0,0, -1,-1, 0,0],
+ [-1,-1, 4,4, 5,5, 4,4, -1,-1],[-1,-1, 4,4, 5,5, 4,4, -1,-1],
+ [0,0, -1,-1, 0,0, -1,-1, 0,0],[0,0, -1,-1, 0,0, -1,-1, 0,0],
+ [0,0, 0,0, 1/2,1/2, 0,0, 0,0],[0,0, 0,0, 1/2,1/2, 0,0, 0,0]]) / 16
+
+hq_quad_r_and_b_g_brow_rcol = hq_quad_r_and_b_g_rrow_bcol.T
+
+hq_quad_r_b = np.array([[0,0, 0,0, -3/2,-3/2, 0,0, 0,0],[0,0, 0,0, -3/2,-3/2, 0,0, 0,0],
+ [0,0, 2,2, 0,0, 2,2, 0,0],[0,0, 2,2, 0,0, 2,2, 0,0],
+ [-3/2,-3/2, 0,0, 6,6, 0,0, -3/2,-3/2],[-3/2,-3/2, 0,0, 6,6, 0,0, -3/2,-3/2],
+ [0,0, 2,2, 0,0, 2,2, 0,0],[0,0, 2,2, 0,0, 2,2, 0,0],
+ [0,0, 0,0, -3/2,-3/2, 0,0, 0,0],[0,0, 0,0, -3/2,-3/2, 0,0, 0,0]]) / 16
+
+
+## Calcul the value of one pixel of the 2x2 matrix independentely
+# A = up left pixel / B = up right pixel / C = down left pixel / D = down right pixel
+
+A_hq_quad_g_r_and_b = np.array([[0,0, 0,0, -1,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [-1,0, 2,0, 4,0, 2,0, -1,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, -1,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+B_hq_quad_g_r_and_b = np.array([[0,0, 0,0, 0,-1, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 0,2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,-1, 0,2, 0,4, 0,2, 0,-1],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 0,2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 0,-1, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+C_hq_quad_g_r_and_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -1,0, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 2,0, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[-1,0, 2,0, 4,0, 2,0, -1,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 2,0, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -1,0, 0,0, 0,0]]) / 8
+
+D_hq_quad_g_r_and_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-1, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,2, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,-1, 0,2, 0,4, 0,2, 0,-1],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,2, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-1, 0,0, 0,0]]) / 8
+
+
+A_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 1/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, -1,0, 0,0, -1,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [-1,0, 4,0, 5,0, 4,0, -1,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, -1,0, 0,0, -1,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 1/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+B_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 0,1/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,-1, 0,0, 0,-1, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,-1, 0,4, 0,5, 0,4, 0,-1],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,-1, 0,0, 0,-1, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 0,-1/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+C_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 1/2,0, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, -1,0, 0,0, -1,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[-1,0, 4,0, 5,0, 4,0, -1,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, -1,0, 0,0, -1,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 1/2,0, 0,0, 0,0]]) / 8
+
+D_hq_quad_r_and_b_g_rrow_bcol = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,1/2, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,-1, 0,0, 0,-1, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,-1, 0,4, 0,5, 0,4, 0,-1],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,-1, 0,0, 0,-1, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-1/2, 0,0, 0,0]]) / 8
+
+
+A_hq_quad_r_and_b_g_brow_rcol = A_hq_quad_r_and_b_g_rrow_bcol.T
+B_hq_quad_r_and_b_g_brow_rcol = B_hq_quad_r_and_b_g_rrow_bcol.T
+C_hq_quad_r_and_b_g_brow_rcol = C_hq_quad_r_and_b_g_rrow_bcol.T
+D_hq_quad_r_and_b_g_brow_rcol = D_hq_quad_r_and_b_g_rrow_bcol.T
+
+
+
+
+A_hq_quad_r_b = np.array([[0,0, 0,0, -3/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 2,0, 0,0, 2,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [-3/2,0, 0,0, 6,0, 0,0, -3/2,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 2,0, 0,0, 2,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, -3/2,0, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+B_hq_quad_r_b = np.array([[0,0, 0,0, 0,-3/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,2, 0,0, 0,2, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,-3/2, 0,0, 0,6, 0,0, 0,-3/2],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,2, 0,0, 0,2, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0],
+ [0,0, 0,0, 0,-3/2, 0,0, 0,0],[0,0, 0,0, 0,0, 0,0, 0,0]]) / 8
+
+
+C_hq_quad_r_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -3/2,0, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 2,0, 0,0, 2,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[-3/2,0, 0,0, 6,0, 0,0, -3/2,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 2,0, 0,0, 2,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, -3/2,0, 0,0, 0,0]]) / 8
+
+D_hq_quad_r_b = np.array([[0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-3/2, 0,0, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,2, 0,0, 0,2, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,-3/2, 0,0, 0,6, 0,0, 0,-3/2],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,2, 0,0, 0,2, 0,0],
+ [0,0, 0,0, 0,0, 0,0, 0,0],[0,0, 0,0, 0,-3/2, 0,0, 0,0]]) / 8
+
+
+
+####
+####
+####
+
+#### #### #### #############
+#### ###### #### ##################
+#### ######## #### ####################
+#### ########## #### #### ########
+#### ############ #### #### ####
+#### #### ######## #### #### ####
+#### #### ######## #### #### ####
+#### #### ######## #### #### ####
+#### #### ## ###### #### #### ######
+#### #### #### ## #### #### ############
+#### #### ###### #### #### ##########
+#### #### ########## #### #### ########
+#### #### ######## #### ####
+#### #### ############ ####
+#### #### ########## ####
+#### #### ######## ####
+#### #### ###### ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller
diff --git a/src/methods/babacar/reconstruct.py b/src/methods/babacar/reconstruct.py
new file mode 100644
index 0000000000000000000000000000000000000000..acd1cb1227d3232f5f8862f30f29578424ce7092
--- /dev/null
+++ b/src/methods/babacar/reconstruct.py
@@ -0,0 +1,54 @@
+"""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
+import src.methods.babacar.demoisaicing_fct as b
+
+
+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.
+
+ input_shape = (y.shape[0], y.shape[1], 3)
+ op = CFA(cfa, input_shape)
+ res = b.High_Quality_Linear_Interpolation(op, y)
+ return res
+
+
+####
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+#### #### ## ###### #### #### ######
+#### #### #### ## #### #### ############
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+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller
diff --git a/src/methods/template/reconstruct.py b/src/methods/template/reconstruct.py
index a97bd3f6e3c68df763b36c46b2727461af078bd2..73454e4b33143911d637056cdc1f49f6f0efd504 100644
--- a/src/methods/template/reconstruct.py
+++ b/src/methods/template/reconstruct.py
@@ -23,7 +23,6 @@ def run_reconstruction(y: np.ndarray, cfa: str) -> np.ndarray:
# TODO
input_shape = (y.shape[0], y.shape[1], 3)
op = CFA(cfa, input_shape)
-
return np.zeros(op.input_shape)