diff --git a/modules/deeplearning/cnn_cld_frac.py b/modules/deeplearning/cnn_cld_frac.py
index 7a3271bb0ced600f6bc97fef01e8891202ea21f6..d51c45ae55cb6998fb2d2893e81ba3ee4f892b72 100644
--- a/modules/deeplearning/cnn_cld_frac.py
+++ b/modules/deeplearning/cnn_cld_frac.py
@@ -381,13 +381,12 @@ class SRCNN:
         tmp = input_data[:, label_idx, :, :]
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0, tmp)
-        if DO_SMOOTH:
-            tmp = smooth_2d(tmp, sigma=SIGMA)
-            # tmp = median_filter_2d(tmp)
         if DO_ESPCN:
             tmp = tmp[:, slc_y_2, slc_x_2]
         else:  # Half res upsampled to full res:
             tmp = upsample(tmp)
+            # tmp = upsample_mean(tmp)
+            # tmp = tmp[:, slc_y, slc_x]
         if label_param != 'cloud_probability':
             tmp = normalize(tmp, label_param, mean_std_dct)
             if DO_ADD_NOISE:
@@ -405,10 +404,6 @@ class SRCNN:
         # -----------------------------------------------------
         label = input_data[:, label_idx, :, :]
         label = label.copy()
-        # if DO_SMOOTH:
-        #     label = np.where(np.isnan(label), 0, label)
-        #     label = smooth_2d(label, sigma=SIGMA)
-        #     # label = median_filter_2d(label)
         label = label[:, y_128, x_128]
         label = get_label_data(label)