def _rs_filter(sinogram, window, listsign, sigma, size, pad):
"""
Remove stripes using the filtering technique.
"""
sinogram = np.transpose(sinogram)
padded_sino = np.pad(sinogram, ((0, 0), (pad, pad)), mode='reflect')
(_, ncol) = padded_sino.shape
sinosmooth = np.zeros_like(sinogram)
for i, sinolist in enumerate(padded_sino):
sinosmooth[i] = np.real(
ifft(fft(sinolist * listsign) * window) * listsign)[pad:ncol - pad]
sinosharp = sinogram - sinosmooth
sinosmooth_cor = median_filter(sinosmooth, (size, 1))
return np.transpose(sinosmooth_cor + sinosharp)
def _rs_filter(sinogram, window, listsign, sigma, size, pad):
"""
Remove stripes using the filtering technique.
"""
sinogram = np.transpose(sinogram)
padded_sino = np.pad(sinogram, ((0, 0), (pad, pad)), mode='reflect')
(_, ncol) = padded_sino.shape
sinosmooth = np.zeros_like(sinogram)
for i, sinolist in enumerate(padded_sino):
sinosmooth[i] = np.real(
ifft(fft(sinolist * listsign) * window) * listsign)[pad:ncol - pad]
sinosharp = sinogram - sinosmooth
sinosmooth_cor = median_filter(sinosmooth, (size, 1))
return np.transpose(sinosmooth_cor + sinosharp)
def _rs_filter(sinogram, window, listsign, size, dim, pad):
"""
Remove stripes using the filtering technique.
"""
sinogram = np.transpose(sinogram)
padded_sino = np.pad(sinogram, ((0, 0), (pad, pad)), mode='reflect')
(nrow, ncol) = padded_sino.shape
sinosmooth = np.zeros_like(sinogram)
for i, sinolist in enumerate(padded_sino):
sinosmooth[i] = np.real(
ifft(fft(sinolist * listsign) * window) * listsign)[pad:ncol - pad]
sinosharp = sinogram - sinosmooth
matindex = _create_matindex(nrow, ncol - 2 * pad)
sinosmooth_cor = np.transpose(
_rs_sort(np.transpose(sinosmooth), size, matindex, dim))
return np.transpose(sinosmooth_cor + sinosharp)
def _remove_stripe_fw(tomo, level, wname, sigma, pad):
dx, dy, dz = tomo.shape
nx = dx
if pad:
nx = dx + dx // 8
xshift = int((nx - dx) // 2)
num_jobs = tomo.shape[1]
for m in range(num_jobs):
sli = np.zeros((nx, dz), dtype='float32')
sli[xshift:dx + xshift] = tomo[:, m, :]
# Wavelet decomposition.
cH = []
cV = []
cD = []
for n in range(level):
sli, (cHt, cVt, cDt) = pywt.dwt2(sli, wname)
cH.append(cHt)
cV.append(cVt)
cD.append(cDt)
# FFT transform of horizontal frequency bands.
for n in range(level):
# FFT
fcV = np.fft.fftshift(fft(cV[n], axis=0, extra_info=num_jobs))
my, mx = fcV.shape
# Damping of ring artifact information.
y_hat = (np.arange(-my, my, 2, dtype='float32') + 1) / 2
damp = -np.expm1(-np.square(y_hat) / (2 * np.square(sigma)))
fcV *= np.transpose(np.tile(damp, (mx, 1)))
# Inverse FFT.
cV[n] = np.real(ifft(np.fft.ifftshift(
fcV), axis=0, extra_info=num_jobs))
# Wavelet reconstruction.
for n in range(level)[::-1]:
sli = sli[0:cH[n].shape[0], 0:cH[n].shape[1]]
sli = pywt.idwt2((sli, (cH[n], cV[n], cD[n])), wname)
tomo[:, m, :] = sli[xshift:dx + xshift, 0:dz]
def _remove_stripe_fw(tomo, level, wname, sigma, pad):
dx, dy, dz = tomo.shape
nx = dx
if pad:
nx = dx + dx // 8
xshift = int((nx - dx) // 2)
num_jobs = tomo.shape[1]
for m in range(num_jobs):
sli = np.zeros((nx, dz), dtype='float32')
sli[xshift:dx + xshift] = tomo[:, m, :]
# Wavelet decomposition.
cH = []
cV = []
cD = []
for n in range(level):
sli, (cHt, cVt, cDt) = pywt.dwt2(sli, wname)
cH.append(cHt)
cV.append(cVt)
cD.append(cDt)
# FFT transform of horizontal frequency bands.
for n in range(level):
# FFT
fcV = np.fft.fftshift(fft(cV[n], axis=0, extra_info=num_jobs))
my, mx = fcV.shape
# Damping of ring artifact information.
y_hat = (np.arange(-my, my, 2, dtype='float32') + 1) / 2
damp = -np.expm1(-np.square(y_hat) / (2 * np.square(sigma)))
fcV *= np.transpose(np.tile(damp, (mx, 1)))
# Inverse FFT.
cV[n] = np.real(
ifft(np.fft.ifftshift(fcV), axis=0, extra_info=num_jobs))
# Wavelet reconstruction.
for n in range(level)[::-1]:
sli = sli[0:cH[n].shape[0], 0:cH[n].shape[1]]
sli = pywt.idwt2((sli, (cH[n], cV[n], cD[n])), wname)
tomo[:, m, :] = sli[xshift:dx + xshift, 0:dz]