diff --git a/lerobot/common/datasets/transforms.py b/lerobot/common/datasets/transforms.py
index e4ba980c..11499a51 100644
--- a/lerobot/common/datasets/transforms.py
+++ b/lerobot/common/datasets/transforms.py
@@ -1,8 +1,9 @@
-from typing import Any, Callable, Sequence
+from typing import Any, Callable, Dict, Sequence
 
 import torch
 from torchvision.transforms import v2
 from torchvision.transforms.v2 import Transform
+from torchvision.transforms.v2 import functional as F  # noqa: N812
 
 
 class RandomSubsetApply(Transform):
@@ -45,17 +46,39 @@ class RandomSubsetApply(Transform):
         return "\n".join(format_string)
 
 
-def make_transforms(cfg):
-    image_transforms = []
-    if "colorjitter" in cfg.list:
-        image_transforms.append(
-            v2.ColorJitter(brightness=cfg.colorjitter_factor, contrast=cfg.colorjitter_factor)
-        )
-    if "sharpness" in cfg.list:
-        image_transforms.append(v2.RandomAdjustSharpness(cfg.sharpness_factor, p=cfg.sharpness_p))
-    if "blur" in cfg.list:
-        image_transforms.append(v2.RandomAdjustSharpness(cfg.blur_factor, p=cfg.blur_p))
+class RangeRandomSharpness(Transform):
+    """Similar to RandomAdjustSharpness but with p=1 and a sharpness_factor sampled randomly
+    each time in [range_min, range_max].
 
+    If the input is a :class:`torch.Tensor`,
+    it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions.
+    """
+
+    def __init__(self, range_min: float, range_max) -> None:
+        super().__init__()
+        self.range_min, self.range_max = self._check_input(range_min, range_max)
+
+    def _check_input(self, range_min, range_max):
+        if range_min < 0:
+            raise ValueError("range_min must be non negative.")
+        if range_min > range_max:
+            raise ValueError("range_max must greater or equal to range_min")
+        return range_min, range_max
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        sharpness_factor = self.range_min + (self.range_max - self.range_min) * torch.rand(1)
+        return self._call_kernel(F.adjust_sharpness, inpt, sharpness_factor=sharpness_factor)
+
+
+def make_transforms(cfg):
+    image_transforms = [
+        v2.ColorJitter(brightness=(cfg.brightness.min, cfg.brightness.max)),
+        v2.ColorJitter(contrast=(cfg.contrast.min, cfg.contrast.max)),
+        v2.ColorJitter(saturation=(cfg.saturation.min, cfg.saturation.max)),
+        v2.ColorJitter(hue=(cfg.hue.min, cfg.hue.max)),
+        RangeRandomSharpness(cfg.sharpness.min, cfg.sharpness.max),
+    ]
+    # WIP
     return v2.Compose(
         [RandomSubsetApply(image_transforms, n_subset=cfg.n_subset), v2.ToDtype(torch.float32, scale=True)]
     )
diff --git a/lerobot/configs/default.yaml b/lerobot/configs/default.yaml
index 04c687bd..3c195c80 100644
--- a/lerobot/configs/default.yaml
+++ b/lerobot/configs/default.yaml
@@ -60,14 +60,26 @@ wandb:
 
 image_transform:
   enable: false
-  colorjitter_factor: 0.5
-  colorjitter_p: 1.O
-  sharpness_factor: 3.0
-  # Should be more than 1, setting parameter to 1 does not change the image
-  sharpness_p: 0.5
-  blur_factor: 0.2
-  # Should be less than 1, setting parameter to 1 does not change the image
-  blur_p: 0.5
-  n_subset: 3
   # Maximum number of transforms to apply
-  list: ["colorjitter", "sharpness", "blur"]
+  max_num_transforms: 3
+  random_order: false
+  brightness:
+    weight: 1
+    min: 0.5
+    max: 1.5
+  contrast:
+    weight: 1
+    min: 0.5
+    max: 1.5
+  saturation:
+    weight: 1
+    min: 0.5
+    max: 1.5
+  hue:
+    weight: 1
+    min: -0.1
+    max: 0.1
+  sharpness:
+    weight: 1
+    min: 0.9
+    max: 1.1