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diff --git a/src/methods/EL-MURR-Theresa/malvar.py b/src/methods/EL-MURR-Theresa/malvar.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a002a8d3dcb77425d8d448d6c30ca4eab0aee4a
--- /dev/null
+++ b/src/methods/EL-MURR-Theresa/malvar.py
@@ -0,0 +1,157 @@
+import numpy as np
+from scipy.signal import correlate2d
+from src.forward_model import CFA
+
+def malvar_he_cutler(y: np.ndarray, op: CFA ) -> np.ndarray:
+ """Performs demosaicing using the malvar-he-cutler algorithm
+
+ Args:
+ op (CFA): CFA operator.
+ y (np.ndarray): Mosaicked image.
+
+ Returns:
+ np.ndarray: Demosaicked image.
+ """
+
+ red_mask, green_mask, blue_mask = [op.mask[:, :, 0], op.mask[:, :, 1], op.mask[:, :, 2]]
+ mosaicked_image = np.float32(y)
+ demosaicked_image = np.empty(op.input_shape)
+
+ if op.cfa == 'quad_bayer':
+ filters = get_quad_bayer_filters()
+ else:
+ filters = get_default_filters()
+
+ demosaicked_image = apply_demosaicking_filters(
+ mosaicked_image,demosaicked_image, red_mask, green_mask, blue_mask, filters
+ )
+
+ return demosaicked_image
+
+def get_quad_bayer_filters():
+ coefficient_scale = 0.03125
+ return {
+ "G_at_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]
+ ]) * coefficient_scale,
+ "R_at_GR_and_B_at_GB": np.array([
+ [0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0.5, 0.5, 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, 0.5, 0.5, 0, 0, 0, 0],
+ [0, 0, 0, 0, 0.5, 0.5, 0, 0, 0, 0]
+ ]) * coefficient_scale,
+ "R_at_GB_and_B_at_GR": np.array([
+ [0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
+ [0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
+ [0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
+ [0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
+ [0.5, 0.5, 0, 0, 5, 5, 0, 0, 0.5, 0.5],
+ [0.5, 0.5, 0, 0, 5, 5, 0, 0, 0.5, 0.5],
+ [0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
+ [0, 0, -1, -1, 4, 4, -1, -1, 0, 0],
+ [0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
+ [0, 0, 0, 0, -1, -1, 0, 0, 0, 0]
+ ]) * coefficient_scale,
+ "R_at_B_and_B_at_R": np.array([
+ [0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0],
+ [0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0],
+ [0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
+ [0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
+ [-1.5, -1.5, 0, 0, 6, 6, 0, 0, -1.5, -1.5],
+ [-1.5, -1.5, 0, 0, 6, 6, 0, 0, -1.5, -1.5],
+ [0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
+ [0, 0, 2, 2, 0, 0, 2, 2, 0, 0],
+ [0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0],
+ [0, 0, 0, 0, -1.5, -1.5, 0, 0, 0, 0]
+ ]) * coefficient_scale,
+ }
+
+def get_default_filters():
+ coefficient_scale = 0.125
+ return {
+ "G_at_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]
+ ]) * coefficient_scale,
+ "R_at_GR_and_B_at_GB": np.array([
+ [0, 0, 0.5, 0, 0],
+ [0, -1, 0, -1, 0],
+ [-1, 4, 5, 4, -1],
+ [0, -1, 0, -1, 0],
+ [0, 0, 0.5, 0, 0]
+ ]) * coefficient_scale,
+ "R_at_GB_and_B_at_GR": np.array([
+ [0, 0, -1, 0, 0],
+ [0, -1, 4, -1, 0],
+ [0.5, 0, 5, 0, 0.5],
+ [0, -1, 4, -1, 0],
+ [0, 0, -1, 0, 0]
+ ]) * coefficient_scale,
+ "R_at_B_and_B_at_R": np.array([
+ [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]
+ ]) * coefficient_scale,
+ }
+
+def apply_demosaicking_filters(image, res, red_mask, green_mask, blue_mask, filters):
+ red_channel = image * red_mask
+ green_channel = image * green_mask
+ blue_channel = image * blue_mask
+
+ # Create the green channel after applying a filter
+ green_channel = np.where(
+ np.logical_or(red_mask == 1, blue_mask == 1),
+ correlate2d(image, filters['G_at_R_and_B'], mode="same", boundary="symm"),
+ green_channel
+ )
+
+
+ # Define masks for extracting pixel values
+ red_row_mask = np.any(red_mask == 1, axis=1)[:, np.newaxis].astype(np.float32)
+ red_col_mask = np.any(red_mask == 1, axis=0)[np.newaxis].astype(np.float32)
+
+ blue_row_mask = np.any(blue_mask == 1, axis=1)[:, np.newaxis].astype(np.float32)
+ blue_col_mask = np.any(blue_mask == 1, axis=0)[np.newaxis].astype(np.float32)
+
+ def update_channel(channel, row_mask, col_mask, filter_key):
+ return np.where(
+ np.logical_and(row_mask == 1, col_mask == 1),
+ correlate2d(image, filters[filter_key], mode="same", boundary="symm"),
+ channel
+ )
+
+# Update the red channel and blue channel
+ red_channel = update_channel(red_channel, red_row_mask, blue_col_mask, 'R_at_GR_and_B_at_GB')
+ red_channel = update_channel(red_channel, blue_row_mask, red_col_mask, 'R_at_GB_and_B_at_GR')
+
+ blue_channel = update_channel(blue_channel, blue_row_mask, red_col_mask, 'R_at_GR_and_B_at_GB')
+ blue_channel = update_channel(blue_channel, red_row_mask, blue_col_mask, 'R_at_GB_and_B_at_GR')
+
+ # Update R channel and B channel again
+ red_channel = update_channel(red_channel, blue_row_mask, blue_col_mask, 'R_at_B_and_B_at_R')
+ blue_channel = update_channel(blue_channel, red_row_mask, red_col_mask, 'R_at_B_and_B_at_R')
+ res[:, :, 0] = red_channel
+ res[:, :, 1] = green_channel
+ res[:, :, 2] = blue_channel
+ return res
\ No newline at end of file
diff --git a/src/methods/EL-MURR-Theresa/reconstruct.py b/src/methods/EL-MURR-Theresa/reconstruct.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a5c73eb13976dc8de7cc60494cda93c77621dd7
--- /dev/null
+++ b/src/methods/EL-MURR-Theresa/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
+from src.methods.el_murr.malvar import malvar_he_cutler
+
+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 = malvar_he_cutler(y,op)
+
+ return res
+
+
+####
+####
+####
+
+#### #### #### #############
+#### ###### #### ##################
+#### ######## #### ####################
+#### ########## #### #### ########
+#### ############ #### #### ####
+#### #### ######## #### #### ####
+#### #### ######## #### #### ####
+#### #### ######## #### #### ####
+#### #### ## ###### #### #### ######
+#### #### #### ## #### #### ############
+#### #### ###### #### #### ##########
+#### #### ########## #### #### ########
+#### #### ######## #### ####
+#### #### ############ ####
+#### #### ########## ####
+#### #### ######## ####
+#### #### ###### ####
+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller