diff --git a/dan/manager/dataset.py b/dan/manager/dataset.py
index 525e637cdc5e369828d8e49262b3ddd1be345ef0..547ada96bb57de6027adddc2b53e4060c370446b 100644
--- a/dan/manager/dataset.py
+++ b/dan/manager/dataset.py
@@ -347,19 +347,6 @@ def apply_preprocessing(sample, preprocessings):
     resize_ratio = [1, 1]
     img = sample["img"]
     for preprocessing in preprocessings:
-        if preprocessing["type"] == "dpi":
-            ratio = preprocessing["target"] / preprocessing["source"]
-            temp_img = img
-            h, w, c = temp_img.shape
-            temp_img = cv2.resize(
-                temp_img, (int(np.ceil(w * ratio)), int(np.ceil(h * ratio)))
-            )
-            if len(temp_img.shape) == 2:
-                temp_img = np.expand_dims(temp_img, axis=2)
-            img = temp_img
-
-            resize_ratio = [ratio, ratio]
-
         if preprocessing["type"] == "to_grayscaled":
             temp_img = img
             h, w, c = temp_img.shape
diff --git a/docs/usage/train/parameters.md b/docs/usage/train/parameters.md
index bc2b55ad09fe6a00e1e246119f07cb1a22403c13..e729c82afd66a5a04bce25a0a7c0b0186ffd6634 100644
--- a/docs/usage/train/parameters.md
+++ b/docs/usage/train/parameters.md
@@ -23,14 +23,6 @@ All hyperparameters are specified and editable in the training scripts (meaning
 Preprocessing is applied before training the network (see `dan/manager/dataset.py`).
 The following transformations are implemented:
 
-* DPI adjustment
-```py
-    {
-        "type": "dpi",
-        "source": 300,
-        "target": 150,
-    }
-```
 * Convert to grayscale
 ```py
 {