diff --git a/mmedit/models/components/refiners/__init__.py b/mmedit/models/components/refiners/__init__.py
index 6f089bfbe3..3ddf6f0bdd 100644
--- a/mmedit/models/components/refiners/__init__.py
+++ b/mmedit/models/components/refiners/__init__.py
@@ -1,4 +1,5 @@
 from .deepfill_refiner import DeepFillRefiner
+from .mlp_refiner import MLPRefiner
 from .plain_refiner import PlainRefiner
 
-__all__ = ['PlainRefiner', 'DeepFillRefiner']
+__all__ = ['PlainRefiner', 'DeepFillRefiner', 'MLPRefiner']
diff --git a/mmedit/models/components/refiners/mlp_refiner.py b/mmedit/models/components/refiners/mlp_refiner.py
new file mode 100644
index 0000000000..1966f6b25b
--- /dev/null
+++ b/mmedit/models/components/refiners/mlp_refiner.py
@@ -0,0 +1,58 @@
+import torch.nn as nn
+from mmcv.runner import load_checkpoint
+
+from mmedit.models.registry import COMPONENTS
+from mmedit.utils import get_root_logger
+
+
+@COMPONENTS.register_module()
+class MLPRefiner(nn.Module):
+    """Multilayer perceptrons (MLPs), refiner used in LIIF.
+
+    Args:
+        in_dim (int): Input dimension.
+        out_dim (int): Output dimension.
+        hidden_list (list[int]): List of hidden dimensions.
+    """
+
+    def __init__(self, in_dim, out_dim, hidden_list):
+        super().__init__()
+        layers = []
+        lastv = in_dim
+        for hidden in hidden_list:
+            layers.append(nn.Linear(lastv, hidden))
+            layers.append(nn.ReLU())
+            lastv = hidden
+        layers.append(nn.Linear(lastv, out_dim))
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        """Forward function.
+
+        Args:
+            x (Tensor): The input of MLP.
+
+        Returns:
+            Tensor: The output of MLP.
+        """
+        shape = x.shape[:-1]
+        x = self.layers(x.view(-1, x.shape[-1]))
+        return x.view(*shape, -1)
+
+    def init_weights(self, pretrained=None, strict=True):
+        """Init weights for models.
+
+        Args:
+            pretrained (str, optional): Path for pretrained weights. If given
+                None, pretrained weights will not be loaded. Defaults to None.
+            strict (boo, optional): Whether strictly load the pretrained model.
+                Defaults to True.
+        """
+        if isinstance(pretrained, str):
+            logger = get_root_logger()
+            load_checkpoint(self, pretrained, strict=strict, logger=logger)
+        elif pretrained is None:
+            pass
+        else:
+            raise TypeError(f'"pretrained" must be a str or None. '
+                            f'But received {type(pretrained)}.')
diff --git a/tests/test_mlp_refiner.py b/tests/test_mlp_refiner.py
new file mode 100644
index 0000000000..5c93436fbc
--- /dev/null
+++ b/tests/test_mlp_refiner.py
@@ -0,0 +1,33 @@
+import torch
+import torch.nn as nn
+
+from mmedit.models.builder import build_component
+
+
+def test_mlp_refiner():
+    model_cfg = dict(
+        type='MLPRefiner', in_dim=8, out_dim=3, hidden_list=[8, 8, 8, 8])
+    mlp = build_component(model_cfg)
+
+    # test attributes
+    assert mlp.__class__.__name__ == 'MLPRefiner'
+
+    # prepare data
+    inputs = torch.rand(2, 8)
+    targets = torch.rand(2, 3)
+    if torch.cuda.is_available():
+        inputs = inputs.cuda()
+        targets = targets.cuda()
+        mlp = mlp.cuda()
+    data_batch = {'in': inputs, 'target': targets}
+    # prepare optimizer
+    criterion = nn.L1Loss()
+    optimizer = torch.optim.Adam(mlp.parameters(), lr=1e-4)
+
+    # test train_step
+    output = mlp.forward(data_batch['in'])
+    assert output.shape == data_batch['target'].shape
+    loss = criterion(output, data_batch['target'])
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()