diff --git a/src/cryo_sbi/inference/train_npe_model.py b/src/cryo_sbi/inference/train_npe_model.py
index 2e9fef9..70e8810 100644
--- a/src/cryo_sbi/inference/train_npe_model.py
+++ b/src/cryo_sbi/inference/train_npe_model.py
@@ -58,7 +58,6 @@ def npe_train_no_saving(
     saving_frequency: int = 20,
     simulation_batch_size: int = 1024,
     gamma: float = 1.0,
-    experimental_particles: Union[str, None] = None,
 ) -> None:
     """
     Train NPE model by simulating training data on the fly.
@@ -119,7 +118,6 @@ def npe_train_no_saving(
     )
 
     loss = NPERobustStatsLoss(estimator, gamma)
-    experimental_particles = torch.load(experimental_particles, map_location=device)
 
     optimizer = optim.AdamW(
         estimator.parameters(), lr=train_config["LEARNING_RATE"], weight_decay=0.001
@@ -143,7 +141,7 @@ def npe_train_no_saving(
                     amp,
                     snr,
                 ) = parameters
-                images = cryo_em_simulator(
+                images, clear_images = cryo_em_simulator(
                     models,
                     indices.to(device, non_blocking=True),
                     quaternions.to(device, non_blocking=True),
@@ -156,17 +154,17 @@ def npe_train_no_saving(
                     num_pixels,
                     pixel_size,
                 )
-                for _indices, _images in zip(
+                for _indices, _images, _clear_images in zip(
                     indices.split(train_config["BATCH_SIZE"]),
                     images.split(train_config["BATCH_SIZE"]),
+                    clear_images.split(train_config["BATCH_SIZE"]),
                 ):
-                    random_indices = torch.randperm(experimental_particles.size(0))[:train_config["BATCH_SIZE"]]
                     losses.append(
                         step(
                             loss(
                                 _indices.to(device, non_blocking=True),
                                 _images.to(device, non_blocking=True),
-                                experimental_particles[random_indices].to(device, non_blocking=True),
+                                _clear_images.to(device, non_blocking=True),
                             )
                         )
                     )
diff --git a/src/cryo_sbi/wpa_simulator/cryo_em_simulator.py b/src/cryo_sbi/wpa_simulator/cryo_em_simulator.py
index efcc7c9..93abcd4 100644
--- a/src/cryo_sbi/wpa_simulator/cryo_em_simulator.py
+++ b/src/cryo_sbi/wpa_simulator/cryo_em_simulator.py
@@ -52,10 +52,11 @@ def cryo_em_simulator(
         num_pixels,
         pixel_size,
     )
-    image = apply_ctf(image, defocus, b_factor, amp, pixel_size)
-    image = add_noise(image, snr)
+    clear_image = apply_ctf(image, defocus, b_factor, amp, pixel_size)
+    image = add_noise(clear_image, snr)
     image = gaussian_normalize_image(image)
-    return image
+    clear_image = gaussian_normalize_image(clear_image)
+    return image, clear_image
 
 
 class CryoEmSimulator:
@@ -159,12 +160,13 @@ def simulate(self, num_sim, indices=None, return_parameters=False, batch_size=No
             parameters[0] = indices
 
         images = []
+        clear_images = []
         if batch_size is None:
             batch_size = num_sim
         for i in range(0, num_sim, batch_size):
             batch_indices = indices[i : i + batch_size]
             batch_parameters = [param[i : i + batch_size] for param in parameters[1:]]
-            batch_images = cryo_em_simulator(
+            batch_images, batch_clear_images = cryo_em_simulator(
                 self._models,
                 batch_indices,
                 *batch_parameters,
@@ -172,10 +174,12 @@ def simulate(self, num_sim, indices=None, return_parameters=False, batch_size=No
                 self._pixel_size,
             )
             images.append(batch_images.cpu())
+            clear_images.append(batch_clear_images.cpu())
 
         images = torch.cat(images, dim=0)
+        clear_images = torch.cat(clear_images, dim=0)
 
         if return_parameters:
-            return images.cpu(), parameters
+            return images.cpu(), clear_images.cpu(), parameters
         else:
-            return images.cpu()
+            return images.cpu(), clear_images.cpu()