Merge branch 'master' into 'master'

Master

See merge request !29

src/methods/inssaf_cherki/other_functions.py

+import numpy as np
+from scipy import signal
+from src.forward_model import CFA
+
+def compute_gradient(channel, points):
+    """ Compute the gradient for the channel based on the specified points. """
+    gradient = np.zeros_like(channel)
+    for point in points:
+        gradient += np.roll(channel, shift=point, axis=(0, 1))
+    gradient /= len(points)
+    gradient -= channel
+    return gradient
+
+def gradient_correction_interpolation(op: CFA, z: np.ndarray,alpha=0.5, beta=0.5, gamma=0.5) -> np.ndarray:
+    # defining bilinear interpolation filters for the different channels (bayer pattern case)
+    bilinear_filter_red = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) / 4
+    bilinear_filter_green = np.array([[0, 1, 0], [1, 4, 1], [0, 1, 0]]) / 4
+    bilinear_filter_blue = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) / 4
+
+    interpolated_img = np.empty(op.input_shape)
+    
+    # applying the convolution product with the bilinear filters
+    interpolated_img[:, :, 0] = signal.convolve2d(z[:, :, 0], bilinear_filter_red,  boundary='symm',mode='same')
+    interpolated_img[:, :, 1] = signal.convolve2d(z[:, :, 1], bilinear_filter_green,  boundary='symm',mode='same')
+    interpolated_img[:, :, 2] = signal.convolve2d(z[:, :, 2], bilinear_filter_blue, boundary='symm', mode='same')
+
+    """ Applying gradient correction to the bilinearly interpolated image. """
+
+
+    R = interpolated_img[:, :, 0]
+    G = interpolated_img[:, :, 1]
+    B = interpolated_img[:, :, 2]
+
+    # computing gradients for R and B channels
+    points_R = [(0, -2), (0, 2), (-2, 0), (2, 0)] 
+    points_B = [(-1, -1), (-1, 1), (1, -1), (1, 1), (0, 0)]  
+    grad_R = compute_gradient(R, points_R)
+    grad_B = compute_gradient(B, points_B)
+
+    G_corrected = np.copy(G)
+    G_corrected[0::2, 0::2] = G[0::2, 0::2] + alpha * grad_R[0::2, 0::2]
+    
+    G_corrected[1::2, 1::2] = G[1::2, 1::2] + gamma * grad_B[1::2, 1::2]
+    
+    # correction of R at G locations and B at G locations
+    R_corrected = np.copy(R)
+    B_corrected = np.copy(B)
+    R_corrected[1::2, 0::2] = R[1::2, 0::2] + beta * grad_R[1::2, 0::2]  
+    R_corrected[0::2, 1::2] = R[0::2, 1::2] + beta * grad_R[0::2, 1::2] 
+    B_corrected[1::2, 0::2] = B[1::2, 0::2] + beta * grad_B[1::2, 0::2] 
+    B_corrected[0::2, 1::2] = B[0::2, 1::2] + beta * grad_B[0::2, 1::2]  
+
+   
+    corrected_image = np.stack((R_corrected, G_corrected, B_corrected), axis=-1)
+
+    return corrected_image
+
+def swapping(op, y):
+    """
+    Convert both the CFA operator's mask and an image from Quad Bayer to Bayer by swapping method.
+
+    Args:
+        op: CFA operator.
+        y (np.ndarray): Mosaicked image.
+
+    Returns:
+        tuple: A tuple containing the updated CFA operator and the converted image.
+    """
+
+    if op.cfa == 'quad_bayer':
+        #  swapping 2 columns every 2 columns
+        op.mask[:, 1::4], op.mask[:, 2::4] = op.mask[:, 2::4].copy(), op.mask[:, 1::4].copy()
+        #  swapping 2 lines every 2 lines
+        op.mask[1::4, :], op.mask[2::4, :] = op.mask[2::4, :].copy(), op.mask[1::4, :].copy()
+        # swapping the diagonal pairs
+        op.mask[1::4, 1::4], op.mask[2::4, 2::4] = op.mask[2::4, 2::4].copy(), op.mask[1::4, 1::4].copy()
+
+    converted_image = y.copy()
+    converted_image[:, 1::4], converted_image[:, 2::4] = converted_image[:, 2::4].copy(), converted_image[:, 1::4].copy()
+    converted_image[1::4, :], converted_image[2::4, :] = converted_image[2::4, :].copy(), converted_image[1::4, :].copy()
+    converted_image[1::4, 1::4], converted_image[2::4, 2::4] = converted_image[2::4, 2::4].copy(), converted_image[1::4, 1::4].copy()
+
+    # updating to bayer pattern
+    op.cfa = 'bayer'
+
+    return op, converted_image    
\ No newline at end of file

src/methods/inssaf_cherki/reconstruct.py

+import numpy as np
+from src.methods.inssaf_cherki.other_functions import gradient_correction_interpolation, swapping, compute_gradient
+from src.forward_model import CFA
+
+
+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)
+
+    if op.cfa == 'quad_bayer':
+        op, y = swapping(op, y)   
+
+    z = op.adjoint(y)
+    reconstructed_image = gradient_correction_interpolation(op,z,alpha=0.5, beta=0.5, gamma=0.5)
+
+    return reconstructed_image
+
+
+####
+####
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+
+# 2023
+# Authors: Mauro Dalla Mura and Matthieu Muller