image and noisy image mse as first mse, visibility changes

deconvolve_func.py

@@ -100,9 +100,7 @@ class DeconvolveWithBead:
         win_size = 7 #default = 7
         #ms_ssim = []
 
-        # minimum = None #[]
-        # previous = None
-        best_mse = np.inf #float('inf')
+        best_mse = np.inf
         minn = None
 
         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"
             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:
                 best_mse = mse[-1]
                 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
             _mssim = ssim(bead, on,
                 win_size = win_size, # okaalsem siis 3,5,7, kui globaalsem võrdlus, siis suurem

pohi.py

@@ -88,6 +88,7 @@ with h5py.File(args.output, 'w') as hdf5_file:
             mssim = []
 
             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)
                 psf_kerneled = gaussian_filter(psf, sigma = j)
                 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}/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])