diff --git a/lightly/transforms/__init__.py b/lightly/transforms/__init__.py
index 338def43a..6794c146f 100644
--- a/lightly/transforms/__init__.py
+++ b/lightly/transforms/__init__.py
@@ -9,6 +9,7 @@
 # All Rights Reserved
 
 from lightly.transforms.aim_transform import AIMTransform
+from lightly.transforms.amplitude_rescale_transform import AmplitudeRescaleTranform
 from lightly.transforms.byol_transform import (
     BYOLTransform,
     BYOLView1Transform,
diff --git a/lightly/transforms/amplitude_rescale_transform.py b/lightly/transforms/amplitude_rescale_transform.py
new file mode 100644
index 000000000..e09128ced
--- /dev/null
+++ b/lightly/transforms/amplitude_rescale_transform.py
@@ -0,0 +1,34 @@
+from typing import Tuple
+
+import numpy as np
+import torch
+from torch import Tensor
+from torch.distributions import Uniform
+
+
+class AmplitudeRescaleTranform:
+    """Implementation of amplitude rescaling transformation.
+
+    This transform will rescale the amplitude of the Fourier Spectrum (`freq_image`) of the image and return it.
+
+    Attributes:
+        dist:
+            Uniform distribution in `[m, n)` from which the scaling value will be selected.
+    """
+
+    def __init__(self, range: Tuple[float, float] = (0.8, 1.75)) -> None:
+        self.dist = Uniform(range[0], range[1])
+
+    def __call__(self, freq_image: Tensor) -> Tensor:
+        amplitude = torch.sqrt(freq_image.real**2 + freq_image.imag**2)
+
+        phase = torch.atan2(freq_image.imag, freq_image.real)
+        # p with shape (H, W)
+        p = self.dist.sample(freq_image.shape[1:]).to(freq_image.device)
+        # Unsqueeze to add channel dimension.
+        amplitude *= p.unsqueeze(0)
+        real = amplitude * torch.cos(phase)
+        imag = amplitude * torch.sin(phase)
+        output = torch.complex(real, imag)
+
+        return output
diff --git a/tests/transforms/test_amplitude_rescale_transform.py b/tests/transforms/test_amplitude_rescale_transform.py
new file mode 100644
index 000000000..0b8d38eaa
--- /dev/null
+++ b/tests/transforms/test_amplitude_rescale_transform.py
@@ -0,0 +1,26 @@
+import numpy as np
+import torch
+
+from lightly.transforms import (
+    AmplitudeRescaleTranform,
+    IRFFT2DTransform,
+    RFFT2DTransform,
+)
+
+
+# Testing function image -> FFT -> AmplitudeRescale.
+# Compare shapes of source and result.
+def test() -> None:
+    image = torch.randn(3, 64, 64)
+
+    rfftTransform = RFFT2DTransform()
+    rfft = rfftTransform(image)
+
+    ampRescaleTf_1 = AmplitudeRescaleTranform()
+    rescaled_rfft_1 = ampRescaleTf_1(rfft)
+
+    ampRescaleTf_2 = AmplitudeRescaleTranform(range=(1.0, 2.0))
+    rescaled_rfft_2 = ampRescaleTf_2(rfft)
+
+    assert rescaled_rfft_1.shape == rfft.shape
+    assert rescaled_rfft_2.shape == rfft.shape