otto/deconv-test
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image and noisy image mse as first mse, visibility changes

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Otto Gustavson
2025-10-24 17:07:41 +03:00
parent 112ae6d5b0
commit cf21390395
2 changed files with 8 additions and 31 deletions
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29
deconvolve_func.py
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@@ -100,9 +100,7 @@ class DeconvolveWithBead:
win_size = 7 #default = 7 win_size = 7 #default = 7
#ms_ssim = [] #ms_ssim = []
# minimum = None #[] best_mse = np.inf
# previous = None
best_mse = np.inf #float('inf')
minn = None minn = None
print("Deconvolving:", psf.min(), psf.max(), psf.sum(), otf.sum(), on.sum()) print("Deconvolving:", psf.min(), psf.max(), psf.sum(), otf.sum(), on.sum())
@@ -116,35 +114,10 @@ class DeconvolveWithBead:
#slicing mse to remove unwanted irrelevant data around the "sõõr" #slicing mse to remove unwanted irrelevant data around the "sõõr"
onsum = on.sum() onsum = on.sum()
# # saving the previous image
# if previous is None:
# minimum = on.copy() #[on]
# else:
# minimum = previous.copy() #[previous]
# previous = on.copy()
if mse[-1] < best_mse: if mse[-1] < best_mse:
best_mse = mse[-1] best_mse = mse[-1]
minn = on.copy() minn = on.copy()
# if len(mse) >= 2 and mse[-2] < mse[-1]:
# minn = minimum
# print("---------------------miinimum-pilt-on-leitud------------------------------------")
# fig = plt.figure()
# ax1 = fig.add_subplot(141)
# ax2 = fig.add_subplot(142)
# ax3 = fig.add_subplot(143)
# ax4 = fig.add_subplot(144)
# ax1.imshow(bead)
# ax2.imshow(im)
# ax3.imshow(on)
# ax4.imshow(bead-on)
# plt.show()
#ms_ssim.append(msssim(on,bead))
itr.append(it) # for iterations itr.append(it) # for iterations
_mssim = ssim(bead, on, _mssim = ssim(bead, on,
win_size = win_size, # okaalsem siis 3,5,7, kui globaalsem võrdlus, siis suurem win_size = win_size, # okaalsem siis 3,5,7, kui globaalsem võrdlus, siis suurem

6
pohi.py
View File

@@ -88,6 +88,7 @@ with h5py.File(args.output, 'w') as hdf5_file:
mssim = [] mssim = []
for j in g_sigma: for j in g_sigma:
MSE = np.sum((noisy_image - scaled_original)**2) / noisy_image.size # adding image and noisy image mse
image_kerneled = gaussian_filter(noisy_image, sigma = j) image_kerneled = gaussian_filter(noisy_image, sigma = j)
psf_kerneled = gaussian_filter(psf, sigma = j) psf_kerneled = gaussian_filter(psf, sigma = j)
print(np.sum(image_kerneled),np.sum(noisy_image), np.sum(psf_kerneled), np.sum(psf)) print(np.sum(image_kerneled),np.sum(noisy_image), np.sum(psf_kerneled), np.sum(psf))
@@ -102,7 +103,10 @@ with h5py.File(args.output, 'w') as hdf5_file:
hdf5_file.create_dataset(f'ch{w:03d}/intensity_{i:08.3f}/SIGMA_{j:03.1f}', data = dec[0]) hdf5_file.create_dataset(f'ch{w:03d}/intensity_{i:08.3f}/SIGMA_{j:03.1f}', data = dec[0])
hdf5_file.create_dataset(f"ch{w:03d}/intensity_{i:08.3f}/minimum_image_{j:03.1f}", data = dec[4]) hdf5_file.create_dataset(f"ch{w:03d}/intensity_{i:08.3f}/minimum_image_{j:03.1f}", data = dec[4])
mses.append(dec[1]) dec[1][-1] = MSE
meansquare = np.concatenate(([dec[1][-1]], dec[1][:-1]))
mses.append(meansquare)
#mses.append(dec[1])
mssim.append(dec[2]) mssim.append(dec[2])