def test_mutual_information():
camera_image = camera()
camera_image_with_noise = add_noise(camera())
mi = mutual_information(camera_image, camera_image, normalised=False)
mi_n = mutual_information(camera_image,
camera_image_with_noise,
normalised=False)
assert mi > mi_n
def test_normalised_mutual_information():
camera_image = camera()
camera_image_with_noise = add_noise(camera())
assert pytest.approx(
mutual_information(camera_image, camera_image, normalised=True), 1)
assert pytest.approx(
mutual_information(camera_image_with_noise,
camera_image_with_noise,
normalised=True),
1,
)
assert (mutual_information(
camera_image, camera_image_with_noise, normalised=True) < 1)
def benchmark_on_image(run_name, folder, image_name, image, methods):
def printscore(header, val1, val2, val3, val4):
print(
f"{header}: \t {val1:.4f} \t {val2:.4f} \t {val3:.4f} \t {val4:.4f}"
)
image = normalise(image.astype(numpy.float32))
gt_numpy_filepath = join(join(folder, 'gt_numpy'),
f'{image_name}' + '.npy')
numpy.save(gt_numpy_filepath, image)
blurred_image, psf_kernel = add_microscope_blur_2d(
image, multi_channel=image.ndim == 3)
noisy_blurred_image = add_poisson_gaussian_noise(blurred_image,
alpha=0.001,
sigma=0.1,
sap=0.01,
quant_bits=10)
blurrynoisy_numpy_filepath = join(join(folder, 'blurrynoisy_numpy'),
f'{image_name}' + '.npy')
numpy.save(blurrynoisy_numpy_filepath, noisy_blurred_image)
blurry_filepath = join(join(folder, 'blurry'), image_name)
save_png(blurry_filepath, blurred_image)
blurrynoisy_filepath = join(join(folder, 'blurrynoisy'), image_name)
save_png(blurrynoisy_filepath, noisy_blurred_image)
method_names = [method.__name__ for method in methods]
# We restore the images with all methods:
restored_image_list = []
with open(join(folder, f"timming_{run_name}.tsv"), "a") as timming_file:
for restore in methods:
restored_cached_filepath = join(
join(folder, 'restored_cache_numpy'),
f'{run_name}_{restore.__name__}_' + image_name + '.npy')
if exists(restored_cached_filepath):
print(
f"File: {restored_cached_filepath} does exists: skipping restoration."
)
restored_image = numpy.load(restored_cached_filepath)
else:
print(
f"File: {restored_cached_filepath} does not exists, restoration started."
)
restored_image, train_time, inf_time = restore(
noisy_blurred_image, psf_kernel)
numpy.save(restored_cached_filepath, restored_image)
timming_file.write(
f"{image_name}\t{restore.__name__}\t{train_time}\t{inf_time}\n"
)
restored_image_list.append(restored_image)
restored_filepath = join(
join(folder, 'restored'),
f'{run_name}_{restore.__name__}_' + image_name)
save_png(restored_filepath, restored_image)
# We compute scores:
with open(join(folder, f"scores_{run_name}.tsv"), "a") as scores_file:
blurred_psnr_value = psnr(image, blurred_image)
blurred_ssim_value = ssim(image, blurred_image)
blurred_mi_value = mutual_information(image, blurred_image)
blurred_smi_value = spectral_mutual_information(image, blurred_image)
noisy_blurred_psnr_value = psnr(image, noisy_blurred_image)
noisy_blurred_ssim_value = ssim(image, noisy_blurred_image)
noisy_blurred_mi_value = mutual_information(image, noisy_blurred_image)
noisy_blurred_smi_value = spectral_mutual_information(
image, noisy_blurred_image)
scores_file.write(
f"{image_name}\tblurry\t{blurred_psnr_value}\t{blurred_ssim_value}\t{blurred_mi_value}\t{blurred_smi_value}\n"
)
scores_file.write(
f"{image_name}\tnoisy&blurred\t{noisy_blurred_psnr_value}\t{noisy_blurred_ssim_value}\t{noisy_blurred_mi_value}\t{noisy_blurred_smi_value}\n"
)
print(
"Below in order: PSNR, norm spectral mutual info, norm mutual info, SSIM: "
)
printscore(
"blurry image \t\t: ",
blurred_psnr_value,
blurred_ssim_value,
blurred_mi_value,
blurred_smi_value,
)
printscore(
"noisy and blurry image \t\t: ",
noisy_blurred_psnr_value,
noisy_blurred_ssim_value,
noisy_blurred_mi_value,
noisy_blurred_smi_value,
)
for restore in methods:
restored_filepath = join(
join(folder, 'restored_cache_numpy'),
f'{run_name}_{restore.__name__}_' + image_name + '.npy')
restored_image = numpy.load(restored_filepath)
psnr_value = psnr(image, restored_image)
ssim_value = ssim(image, restored_image)
mi_value = mutual_information(image, restored_image)
smi_value = spectral_mutual_information(image, restored_image)
printscore(f"restored with {restore.__name__} \t\t: ", psnr_value,
ssim_value, mi_value, smi_value)
scores_file.write(
f"{image_name}\t{restore.__name__}\t{psnr_value}\t{ssim_value}\t{mi_value}\t{smi_value}\n"
)
def demo(image_clipped):
image_clipped = normalise(image_clipped.astype(numpy.float32))
blurred_image, psf_kernel = add_microscope_blur_2d(image_clipped)
# noisy_blurred_image = add_noise(blurred_image, intensity=None, variance=0.01, sap=0.01, clip=True)
noisy_blurred_image = add_poisson_gaussian_noise(blurred_image,
alpha=0.001,
sigma=0.1,
sap=0.01,
quant_bits=10)
for i in range(10):
it_deconv = SSIDeconvolution(
max_epochs=3000,
patience=300,
batch_size=8,
learning_rate=0.01,
normaliser_type='identity',
psf_kernel=psf_kernel,
model_class=UNet,
masking=True,
masking_density=0.01,
loss='l2',
)
start = time.time()
it_deconv.train(noisy_blurred_image)
stop = time.time()
print(f"Training: elapsed time: {stop - start} ")
start = time.time()
deconvolved_image = it_deconv.translate(noisy_blurred_image)
stop = time.time()
print(f"inference: elapsed time: {stop - start} ")
image_clipped = numpy.clip(image_clipped, 0, 1)
deconvolved_image_clipped = numpy.clip(deconvolved_image, 0, 1)
printscore(
"ssi deconv : ",
psnr(image_clipped, deconvolved_image_clipped),
spectral_mutual_information(image_clipped,
deconvolved_image_clipped),
mutual_information(image_clipped, deconvolved_image_clipped),
ssim(image_clipped, deconvolved_image_clipped),
)
print(
"NOTE: if you get a bad results for ssi, blame stochastic optimisation and retry..."
)
print(
" The training is done on the same exact image that we infer on, very few pixels..."
)
print(" Training should be more stable given more data...")
folder = Path(".") / f"{str(i)}"
folder.mkdir(exist_ok=True)
#imwrite(str(fo'image2d.tif',image)
#imwrite('blurred2d.tif', blurred_image)
imwrite(str(folder / 'noisyblurred.tif'), noisy_blurred_image)
#imwrite('lr_deconvolved_image_2.tif',lr_deconvolved_image_2_clipped)
#imwrite('lr_deconvolved_image_5.tif',lr_deconvolved_image_5_clipped)
#imwrite('lr_deconvolved_image_10.tif',lr_deconvolved_image_10_clipped)
#imwrite('lr_deconvolved_image_20.tif',lr_deconvolved_image_20_clipped)
imwrite(str(folder / 'ssi_deconvolved_clipped_image.tif'),
deconvolved_image_clipped)
imwrite(str(folder / 'ssi_deconvolved_image.tif'), deconvolved_image)
def demo(image_clipped):
image_clipped = normalise(image_clipped.astype(numpy.float32))
blurred_image, psf_kernel = add_microscope_blur_2d(image_clipped)
# noisy_blurred_image = add_noise(blurred_image, intensity=None, variance=0.01, sap=0.01, clip=True)
noisy_blurred_image = add_poisson_gaussian_noise(blurred_image,
alpha=0.001,
sigma=0.1,
sap=0.01,
quant_bits=10)
lr = ImageTranslatorLRDeconv(psf_kernel=psf_kernel, backend="cupy")
lr.train(noisy_blurred_image)
lr.max_num_iterations = 2
lr_deconvolved_image_2 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 5
lr_deconvolved_image_5 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 10
lr_deconvolved_image_10 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 20
lr_deconvolved_image_20 = lr.translate(noisy_blurred_image)
it_deconv = SSIDeconvolution(
max_epochs=3000,
patience=300,
batch_size=8,
learning_rate=0.01,
normaliser_type='identity',
psf_kernel=psf_kernel,
model_class=UNet,
masking=True,
masking_density=0.01,
loss='l2',
)
start = time.time()
it_deconv.train(noisy_blurred_image)
stop = time.time()
print(f"Training: elapsed time: {stop - start} ")
start = time.time()
deconvolved_image = it_deconv.translate(noisy_blurred_image)
stop = time.time()
print(f"inference: elapsed time: {stop - start} ")
image_clipped = numpy.clip(image_clipped, 0, 1)
lr_deconvolved_image_2_clipped = numpy.clip(lr_deconvolved_image_2, 0, 1)
lr_deconvolved_image_5_clipped = numpy.clip(lr_deconvolved_image_5, 0, 1)
lr_deconvolved_image_10_clipped = numpy.clip(lr_deconvolved_image_10, 0, 1)
lr_deconvolved_image_20_clipped = numpy.clip(lr_deconvolved_image_20, 0, 1)
deconvolved_image_clipped = numpy.clip(deconvolved_image, 0, 1)
print(
"Below in order: PSNR, norm spectral mutual info, norm mutual info, SSIM: "
)
printscore(
"blurry image : ",
psnr(image_clipped, blurred_image),
spectral_mutual_information(image_clipped, blurred_image),
mutual_information(image_clipped, blurred_image),
ssim(image_clipped, blurred_image),
)
printscore(
"noisy and blurry image: ",
psnr(image_clipped, noisy_blurred_image),
spectral_mutual_information(image_clipped, noisy_blurred_image),
mutual_information(image_clipped, noisy_blurred_image),
ssim(image_clipped, noisy_blurred_image),
)
printscore(
"lr deconv (n=2) : ",
psnr(image_clipped, lr_deconvolved_image_2_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_2_clipped),
mutual_information(image_clipped, lr_deconvolved_image_2_clipped),
ssim(image_clipped, lr_deconvolved_image_2_clipped),
)
printscore(
"lr deconv (n=5) : ",
psnr(image_clipped, lr_deconvolved_image_5_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_5_clipped),
mutual_information(image_clipped, lr_deconvolved_image_5_clipped),
ssim(image_clipped, lr_deconvolved_image_5_clipped),
)
printscore(
"lr deconv (n=10) : ",
psnr(image_clipped, lr_deconvolved_image_10_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_10_clipped),
mutual_information(image_clipped, lr_deconvolved_image_10_clipped),
ssim(image_clipped, lr_deconvolved_image_10_clipped),
)
printscore(
"lr deconv (n=20) : ",
psnr(image_clipped, lr_deconvolved_image_20_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_20_clipped),
mutual_information(image_clipped, lr_deconvolved_image_20_clipped),
ssim(image_clipped, lr_deconvolved_image_20_clipped),
)
printscore(
"ssi deconv : ",
psnr(image_clipped, deconvolved_image_clipped),
spectral_mutual_information(image_clipped, deconvolved_image_clipped),
mutual_information(image_clipped, deconvolved_image_clipped),
ssim(image_clipped, deconvolved_image_clipped),
)
print(
"NOTE: if you get a bad results for ssi, blame stochastic optimisation and retry..."
)
print(
" The training is done on the same exact image that we infer on, very few pixels..."
)
print(" Training should be more stable given more data...")
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image, name='image')
viewer.add_image(blurred_image, name='blurred')
viewer.add_image(noisy_blurred_image, name='noisy_blurred_image')
viewer.add_image(lr_deconvolved_image_2_clipped,
name='lr_deconvolved_image_2')
viewer.add_image(lr_deconvolved_image_5_clipped,
name='lr_deconvolved_image_5')
viewer.add_image(lr_deconvolved_image_10_clipped,
name='lr_deconvolved_image_10')
viewer.add_image(lr_deconvolved_image_20_clipped,
name='lr_deconvolved_image_20')
viewer.add_image(deconvolved_image_clipped,
name='ssi_deconvolved_image')
def demo(image_clipped):
image_clipped = normalise(image_clipped.astype(numpy.float32))
blurred_image, psf_kernel = add_microscope_blur_3d(image_clipped)
noisy_blurred_image = add_poisson_gaussian_noise(blurred_image,
alpha=0.001,
sigma=0.1,
sap=0.01,
quant_bits=10)
lr = ImageTranslatorLRDeconv(psf_kernel=psf_kernel, backend="cupy")
lr.train(noisy_blurred_image)
# lr.max_num_iterations=2
# lr_deconvolved_image_2 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 5
lr_deconvolved_image_5 = lr.translate(noisy_blurred_image)
# lr.max_num_iterations=10
# lr_deconvolved_image_10 = lr.translate(noisy_blurred_image)
# lr.max_num_iterations=20
# lr_deconvolved_image_20 = lr.translate(noisy_blurred_image)
it_deconv = SSIDeconvolution(
max_epochs=3000,
patience=300,
batch_size=8,
learning_rate=0.01,
normaliser_type="identity",
psf_kernel=psf_kernel,
model_class=UNet,
masking=True,
masking_density=0.01,
loss="l2",
)
start = time.time()
it_deconv.train(noisy_blurred_image)
stop = time.time()
print(f"Training: elapsed time: {stop - start} ")
start = time.time()
deconvolved_image = it_deconv.translate(noisy_blurred_image)
stop = time.time()
print(f"inference: elapsed time: {stop - start} ")
image_clipped = numpy.clip(image_clipped, 0, 1)
# lr_deconvolved_image_2_clipped = numpy.clip(lr_deconvolved_image_2, 0, 1)
lr_deconvolved_image_5_clipped = numpy.clip(lr_deconvolved_image_5, 0, 1)
# lr_deconvolved_image_10_clipped = numpy.clip(lr_deconvolved_image_10, 0, 1)
# lr_deconvolved_image_20_clipped = numpy.clip(lr_deconvolved_image_20, 0, 1)
deconvolved_image_clipped = numpy.clip(deconvolved_image, 0, 1)
columns = ["PSNR", "norm spectral mutual info", "norm mutual info", "SSIM"]
print_header(columns)
print_score(
"blurry image",
psnr(image_clipped, blurred_image),
spectral_mutual_information(image_clipped, blurred_image),
mutual_information(image_clipped, blurred_image),
ssim(image_clipped, blurred_image),
)
print_score(
"noisy and blurry image",
psnr(image_clipped, noisy_blurred_image),
spectral_mutual_information(image_clipped, noisy_blurred_image),
mutual_information(image_clipped, noisy_blurred_image),
ssim(image_clipped, noisy_blurred_image),
)
# print_score(
# "lr deconv (n=2)",
# psnr(image_clipped, lr_deconvolved_image_2_clipped),
# spectral_mutual_information(image_clipped, lr_deconvolved_image_2_clipped),
# mutual_information(image_clipped, lr_deconvolved_image_2_clipped),
# ssim(image_clipped, lr_deconvolved_image_2_clipped),
# )
print_score(
"lr deconv (n=5)",
psnr(image_clipped, lr_deconvolved_image_5_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_5_clipped),
mutual_information(image_clipped, lr_deconvolved_image_5_clipped),
ssim(image_clipped, lr_deconvolved_image_5_clipped),
)
# print_score(
# "lr deconv (n=10)",
# psnr(image_clipped, lr_deconvolved_image_10_clipped),
# spectral_mutual_information(image_clipped, lr_deconvolved_image_10_clipped),
# mutual_information(image_clipped, lr_deconvolved_image_10_clipped),
# ssim(image_clipped, lr_deconvolved_image_10_clipped),
# )
#
# print_score(
# "lr deconv (n=20)",
# psnr(image_clipped, lr_deconvolved_image_20_clipped),
# spectral_mutual_information(image_clipped, lr_deconvolved_image_20_clipped),
# mutual_information(image_clipped, lr_deconvolved_image_20_clipped),
# ssim(image_clipped, lr_deconvolved_image_20_clipped),
# )
print_score(
"ssi deconv",
psnr(image_clipped, deconvolved_image_clipped),
spectral_mutual_information(image_clipped, deconvolved_image_clipped),
mutual_information(image_clipped, deconvolved_image_clipped),
ssim(image_clipped, deconvolved_image_clipped),
)
print(
"NOTE: if you get a bad results for ssi, blame stochastic optimisation and retry..."
)
print(
" The training is done on the same exact image that we infer on, very few pixels..."
)
print(" Training should be more stable given more data...")
if use_napari:
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image_clipped, name="image")
viewer.add_image(blurred_image, name="blurred")
viewer.add_image(noisy_blurred_image, name="noisy_blurred_image")
# viewer.add_image(lr_deconvolved_image_2_clipped, name='lr_deconvolved_image_2')
viewer.add_image(lr_deconvolved_image_5_clipped,
name="lr_deconvolved_image_5")
# viewer.add_image(lr_deconvolved_image_10_clipped, name='lr_deconvolved_image_10')
# viewer.add_image(lr_deconvolved_image_20_clipped, name='lr_deconvolved_image_20')
viewer.add_image(deconvolved_image_clipped,
name="ssi_deconvolved_image")
def demo(
image_clipped: np.ndarray,
two_pass: bool = False,
inv_mse_before_forward_model: bool = False,
inv_mse_lambda: float = 2.0,
learning_rate: float = 0.01,
max_epochs: int = 3000,
patience: int = 1000,
masking_density: float = 0.01,
training_noise: float = 0.1,
output_dir: str = "demo_results",
loss: str = "l2",
check: bool = False,
optimizer: str = "esadam",
scheduler: str = "plateau",
clip_before_psf: bool = True,
fft_psf: Union[str, bool] = "auto",
standardize: bool = False,
amp: bool = False,
):
image_clipped = normalise(image_clipped.astype(numpy.float32))
blurred_image, psf_kernel = add_microscope_blur_2d(image_clipped)
# noisy_blurred_image = add_noise(blurred_image, intensity=None, variance=0.01, sap=0.01, clip=True)
noisy_blurred_image = add_poisson_gaussian_noise(blurred_image,
alpha=0.001,
sigma=0.1,
sap=0.01,
quant_bits=10)
lr = ImageTranslatorLRDeconv(psf_kernel=psf_kernel, backend="cupy")
lr.train(noisy_blurred_image)
lr.max_num_iterations = 2
lr_deconvolved_image_2 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 5
lr_deconvolved_image_5 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 10
lr_deconvolved_image_10 = lr.translate(noisy_blurred_image)
lr.max_num_iterations = 20
lr_deconvolved_image_20 = lr.translate(noisy_blurred_image)
it_deconv = SSIDeconvolution(
max_epochs=max_epochs,
patience=patience,
batch_size=8,
learning_rate=learning_rate,
normaliser_type="identity",
psf_kernel=psf_kernel,
model_class=UNet,
masking=True,
masking_density=masking_density,
training_noise=training_noise,
loss=loss,
two_pass=two_pass,
inv_mse_before_forward_model=inv_mse_before_forward_model,
inv_mse_lambda=inv_mse_lambda,
check=check,
optimizer=optimizer,
scheduler=scheduler,
clip_before_psf=clip_before_psf,
fft_psf=fft_psf,
standardize_image=standardize,
amp=amp,
)
start = time.time()
it_deconv.train(noisy_blurred_image)
stop = time.time()
print(f"Training: elapsed time: {stop - start} ")
if not check:
wandb.run.summary["training_time"] = stop - start
start = time.time()
deconvolved_image = it_deconv.translate(noisy_blurred_image)
stop = time.time()
print(f"inference: elapsed time: {stop - start} ")
if not check:
wandb.run.summary["inference_time"] = stop - start
image_clipped = numpy.clip(image_clipped, 0, 1)
lr_deconvolved_image_2_clipped = numpy.clip(lr_deconvolved_image_2, 0, 1)
lr_deconvolved_image_5_clipped = numpy.clip(lr_deconvolved_image_5, 0, 1)
lr_deconvolved_image_10_clipped = numpy.clip(lr_deconvolved_image_10, 0, 1)
lr_deconvolved_image_20_clipped = numpy.clip(lr_deconvolved_image_20, 0, 1)
deconvolved_image_clipped = numpy.clip(deconvolved_image, 0, 1)
columns = ["PSNR", "norm spectral mutual info", "norm mutual info", "SSIM"]
print_header(columns)
print_score(
"blurry image",
psnr(image_clipped, blurred_image),
spectral_mutual_information(image_clipped, blurred_image),
mutual_information(image_clipped, blurred_image),
ssim(image_clipped, blurred_image),
)
print_score(
"noisy and blurry image",
psnr(image_clipped, noisy_blurred_image),
spectral_mutual_information(image_clipped, noisy_blurred_image),
mutual_information(image_clipped, noisy_blurred_image),
ssim(image_clipped, noisy_blurred_image),
)
print_score(
"lr deconv (n=2)",
psnr(image_clipped, lr_deconvolved_image_2_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_2_clipped),
mutual_information(image_clipped, lr_deconvolved_image_2_clipped),
ssim(image_clipped, lr_deconvolved_image_2_clipped),
)
print_score(
"lr deconv (n=5)",
psnr(image_clipped, lr_deconvolved_image_5_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_5_clipped),
mutual_information(image_clipped, lr_deconvolved_image_5_clipped),
ssim(image_clipped, lr_deconvolved_image_5_clipped),
)
print_score(
"lr deconv (n=10)",
psnr(image_clipped, lr_deconvolved_image_10_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_10_clipped),
mutual_information(image_clipped, lr_deconvolved_image_10_clipped),
ssim(image_clipped, lr_deconvolved_image_10_clipped),
)
print_score(
"lr deconv (n=20)",
psnr(image_clipped, lr_deconvolved_image_20_clipped),
spectral_mutual_information(image_clipped,
lr_deconvolved_image_20_clipped),
mutual_information(image_clipped, lr_deconvolved_image_20_clipped),
ssim(image_clipped, lr_deconvolved_image_20_clipped),
)
psnr_deconv = psnr(image_clipped, deconvolved_image_clipped)
smi_deconv = spectral_mutual_information(image_clipped,
deconvolved_image_clipped)
mi_deconv = mutual_information(image_clipped, deconvolved_image_clipped)
ssim_deconv = ssim(image_clipped, deconvolved_image_clipped)
if not check:
wandb.run.summary["psnr"] = psnr_deconv
wandb.run.summary["smi"] = smi_deconv
wandb.run.summary["mi"] = mi_deconv
wandb.run.summary["ssim"] = ssim_deconv
print_score(
"ssi deconv",
psnr_deconv,
smi_deconv,
mi_deconv,
ssim_deconv,
)
print(
"NOTE: if you get a bad results for ssi, blame stochastic optimisation and retry..."
)
print(
" The training is done on the same exact image that we infer on, very few pixels..."
)
print(" Training should be more stable given more data...")
if use_napari:
with napari.gui_qt():
viewer = napari.Viewer()
viewer.add_image(image_clipped, name="image")
viewer.add_image(blurred_image, name="blurred")
viewer.add_image(noisy_blurred_image, name="noisy_blurred_image")
viewer.add_image(lr_deconvolved_image_2_clipped,
name="lr_deconvolved_image_2")
viewer.add_image(lr_deconvolved_image_5_clipped,
name="lr_deconvolved_image_5")
viewer.add_image(lr_deconvolved_image_10_clipped,
name="lr_deconvolved_image_10")
viewer.add_image(lr_deconvolved_image_20_clipped,
name="lr_deconvolved_image_20")
viewer.add_image(deconvolved_image_clipped,
name="ssi_deconvolved_image")
else:
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
imwrite(output_dir / "image.png", image_clipped, format="png")
imwrite(output_dir / "blurred.png", blurred_image, format="png")
imwrite(output_dir / "noisy_blurred_image.png",
noisy_blurred_image,
format="png")
imwrite(
output_dir / "lr_deconvolved_image_2.png",
lr_deconvolved_image_2_clipped,
format="png",
)
imwrite(
output_dir / "lr_deconvolved_image_5.png",
lr_deconvolved_image_5_clipped,
format="png",
)
imwrite(
output_dir / "lr_deconvolved_image_10.png",
lr_deconvolved_image_10_clipped,
format="png",
)
imwrite(
output_dir / "lr_deconvolved_image_20.png",
lr_deconvolved_image_20_clipped,
format="png",
)
imwrite(
output_dir / "ssi_deconvolved_image.png",
deconvolved_image_clipped,
format="png",
)