diff --git a/tests/test_image_utils.py b/tests/test_image_utils.py
index e02273f9747e9a310a0c17594b92834f3070e8de..420fdf043bc9fe7bba1ee223b685698ee3010e03 100644
--- a/tests/test_image_utils.py
+++ b/tests/test_image_utils.py
@@ -6,7 +6,7 @@
 import numpy
 import PIL.Image
 
-from ptbench.data.image_utils import RemoveBlackBorders
+from ptbench.data.image_utils import remove_black_borders
 
 
 def test_remove_black_borders(datadir):
@@ -15,8 +15,7 @@ def test_remove_black_borders(datadir):
     raw_with_black_border = PIL.Image.open(data_file)
 
     # Remove the black border
-    rbb = RemoveBlackBorders()
-    raw_rbb_removed = rbb(raw_with_black_border)
+    raw_rbb_removed = remove_black_borders(raw_with_black_border)
 
     # Get the same sample without black border
     data_file_2 = str(datadir / "raw_without_black_border.png")
@@ -29,23 +28,13 @@ def test_remove_black_borders(datadir):
     numpy.testing.assert_array_equal(raw_without_black_border, raw_rbb_removed)
 
 
-# def test_load_pil_16bit(datadir):
-#     # If the ratio is higher 0.5, image is probably clipped
-#     Level16to8 = SingleAutoLevel16to8()
-#
-#     data_file = str(datadir / "16bits.png")
-#     image = numpy.array(Level16to8(load_pil(data_file)))
-#
-#     count_pixels = numpy.count_nonzero(image)
-#     count_max_value = numpy.count_nonzero(image == image.max())
-#
-#     assert count_max_value / count_pixels < 0.5
-#
-#     # It should not do anything to an image already in 8 bits
-#     data_file = str(datadir / "raw_without_black_border.png")
-#     img_loaded = load_pil(data_file)
-#
-#     original_8bits = numpy.array(img_loaded)
-#     leveled_8bits = numpy.array(Level16to8(img_loaded))
-#
-#     numpy.testing.assert_array_equal(original_8bits, leveled_8bits)
+def test_load_pil_16bit(datadir):
+    # If the ratio is higher 0.5, image is probably clipped
+
+    image = PIL.Image.open(datadir / "16bits.png")
+    array = numpy.array(image).astype(numpy.float32) / 65535
+
+    count_pixels = numpy.count_nonzero(array)
+    count_max_value = numpy.count_nonzero(array == array.max())
+
+    assert count_max_value / count_pixels < 0.5