diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
index b9fb0be624..5164fbeccb 100644
--- a/modules/dnn/src/onnx/onnx_importer.cpp
+++ b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -546,6 +546,43 @@ void ONNXImporter::populateNet(Net dstNet)
         {
             replaceLayerParam(layerParams, "size", "local_size");
         }
+        else if (layer_type == "InstanceNormalization")
+        {
+            if (node_proto.input_size() != 3)
+                CV_Error(Error::StsNotImplemented,
+                         "Expected input, scale, bias");
+
+            layerParams.blobs.resize(4);
+            layerParams.blobs[2] = getBlob(node_proto, constBlobs, 1);  // weightData
+            layerParams.blobs[3] = getBlob(node_proto, constBlobs, 2);  // biasData
+            layerParams.set("has_bias", true);
+            layerParams.set("has_weight", true);
+
+            // Get number of channels in input
+            int size = layerParams.blobs[2].total();
+            layerParams.blobs[0] = Mat::zeros(size, 1, CV_32F); // mean
+            layerParams.blobs[1] = Mat::ones(size, 1, CV_32F); // std
+
+            LayerParams mvnParams;
+            mvnParams.name = layerParams.name + "/MVN";
+            mvnParams.type = "MVN";
+            mvnParams.set("eps", layerParams.get<float>("epsilon"));
+            layerParams.erase("epsilon");
+
+            //Create MVN layer
+            int id = dstNet.addLayer(mvnParams.name, mvnParams.type, mvnParams);
+            //Connect to input
+            layerId = layer_id.find(node_proto.input(0));
+            CV_Assert(layerId != layer_id.end());
+            dstNet.connect(layerId->second.layerId, layerId->second.outputId, id, 0);
+            //Add shape
+            layer_id.insert(std::make_pair(mvnParams.name, LayerInfo(id, 0)));
+            outShapes[mvnParams.name] = outShapes[node_proto.input(0)];
+
+            //Replace Batch Norm's input to MVN
+            node_proto.set_input(0, mvnParams.name);
+            layerParams.type = "BatchNorm";
+        }
         else if (layer_type == "BatchNormalization")
         {
             if (node_proto.input_size() != 5)
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index 186239494f..fc38a2378c 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -76,6 +76,14 @@ public:
     }
 };
 
+TEST_P(Test_ONNX_layers, InstanceNorm)
+{
+    if (target == DNN_TARGET_MYRIAD)
+        testONNXModels("instancenorm", npy, 0, 0, false, false);
+    else
+        testONNXModels("instancenorm", npy);
+}
+
 TEST_P(Test_ONNX_layers, MaxPooling)
 {
     testONNXModels("maxpooling");