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import numpy as np
from scipy.signal import convolve2d
from src.forward_model import CFA
def naive_interpolation(op: CFA, y: np.ndarray) -> np.ndarray:
"""Performs a simple interpolation of the lost pixels.
Args:
op (CFA): CFA operator.
y (np.ndarray): Mosaicked image.
Returns:
np.ndarray: Demosaicked image.
"""
res = np.empty(op.input_shape)
z = op.adjoint(y)
res[:, :, 0] = convolve2d(z[:, :, 0], ker_bayer_red_blue, mode='same')
res[:, :, 1] = convolve2d(z[:, :, 1], ker_bayer_green, mode='same')
res[:, :, 2] = convolve2d(z[:, :, 2], ker_bayer_red_blue, mode='same')
return res
def Spectral_difference(op,y):
z = op.adjoint(y)
y_hat = naive_interpolation(op,y)
res = np.empty(op.input_shape)
for l in range(3):
res[:,:,0]+=z[:,:,l]+convolve2d(z[:,:,0]-y_hat[:,:,l]*op.mask[:,:,0], ker_bayer_red_blue, mode='same')*op.mask[:,:,l]
res[:,:,1]+=z[:,:,l]+convolve2d(z[:,:,1]-y_hat[:,:,l]*op.mask[:,:,1], ker_bayer_green, mode='same')*op.mask[:,:,l]
res[:,:,2]+=z[:,:,l]+convolve2d(z[:,:,2]-y_hat[:,:,l]*op.mask[:,:,2], ker_bayer_red_blue, mode='same')*op.mask[:,:,l]
return res
def quad_bayer_to_bayer(y_quad):
y_bayer=np.copy(y_quad)
for i in range(1,y_quad.shape[0],4):
temp = np.copy(y_bayer[i,:])
y_bayer[i,:]=y_bayer[i+1,:]
y_bayer[i+1,:]=temp
for j in range(1,y_quad.shape[1],4):
temp = np.copy(y_bayer[:,j])
y_bayer[:,j]=y_bayer[:,j+1]
y_bayer[:,j+1]=temp
return y_bayer
ker_bayer_red_blue = np.array([[1, 2, 1], [2, 4, 2], [1, 2, 1]]) / 4
ker_bayer_green = np.array([[0, 1, 0], [1, 4, 1], [0, 1, 0]]) / 4