diff --git a/pytorch3dunet/augment/transforms.py b/pytorch3dunet/augment/transforms.py
index f47061b9..527d596b 100644
--- a/pytorch3dunet/augment/transforms.py
+++ b/pytorch3dunet/augment/transforms.py
@@ -546,28 +546,57 @@ def __call__(self, m):
 
 class Normalize:
     """
-    Apply simple min-max scaling to a given input tensor, i.e. shrinks the range of the data in a fixed range of [-1, 1].
+    Apply simple min-max scaling to a given input tensor, i.e. shrinks the range of the data
+    in a fixed range of [-1, 1] or in case of norm01==True to [0, 1]. In addition, data can be
+    clipped by specifying min_value/max_value either globally using single values or via a
+    list/tuple channelwise if enabled.
     """
 
-    def __init__(self, min_value=None, max_value=None, norm01=False, eps=1e-10, **kwargs):
+    def __init__(self, min_value=None, max_value=None, norm01=False, channelwise=False,
+                 eps=1e-10, **kwargs):
         if min_value is not None and max_value is not None:
-          assert max_value > min_value
+            assert max_value > min_value
         self.min_value = min_value
         self.max_value = max_value
         self.norm01 = norm01
+        self.channelwise = channelwise
         self.eps = eps
 
     def __call__(self, m):
-        if self.min_value is None:
-          min_value = np.min(m)
+        if self.channelwise:
+            # get min/max channelwise
+            axes = list(range(m.ndim))
+            axes = tuple(axes[1:])
+            if self.min_value is None or 'None' in self.min_value:
+                min_value = np.min(m, axis=axes, keepdims=True)
+
+            if self.max_value is None or 'None' in self.max_value:
+                max_value = np.max(m, axis=axes, keepdims=True)
+
+            # check if non None in self.min_value/self.max_value
+            # if present and if so copy value to min_value
+            if self.min_value is not None:
+                for i,v in enumerate(self.min_value):
+                    if v != 'None':
+                        min_value[i] = v
+
+            if self.max_value is not None:
+                for i,v in enumerate(self.max_value):
+                    if v != 'None':
+                        max_value[i] = v
         else:
-          min_value = self.min_value
+            if self.min_value is None:
+                min_value = np.min(m)
+            else:
+                min_value = self.min_value
 
-        if self.max_value is None:
-          max_value = np.max(m)
-        else:
-          max_value = self.max_value
+            if self.max_value is None:
+                max_value = np.max(m)
+            else:
+                max_value = self.max_value
 
+        # calculate norm_0_1 with min_value / max_value with the same dimension
+        # in case of channelwise application
         norm_0_1 = (m - min_value) / (max_value - min_value + self.eps)
 
         if self.norm01 is True: