diff --git a/modules/dnn/src/layers/eltwise_layer.cpp b/modules/dnn/src/layers/eltwise_layer.cpp
index e248b1622d..f077866bdc 100644
--- a/modules/dnn/src/layers/eltwise_layer.cpp
+++ b/modules/dnn/src/layers/eltwise_layer.cpp
@@ -62,6 +62,7 @@ public:
         PROD = 0,
         SUM = 1,
         MAX = 2,
+        DIV = 3
     } op;
     std::vector<float> coeffs;
     bool variableChannels;
@@ -79,6 +80,8 @@ public:
                 op = SUM;
             else if (operation == "max")
                 op = MAX;
+            else if (operation == "div")
+                op = DIV;
             else
                 CV_Error(cv::Error::StsBadArg, "Unknown operation type \"" + operation + "\"");
         }
@@ -271,6 +274,18 @@ public:
                             srcptr0 = (const float*)dstptr;
                         }
                     }
+                    else if( op == DIV )
+                    {
+                        for( k = 1; k < n; k++ )
+                        {
+                            const float* srcptr1 = srcs[k]->ptr<float>() + globalDelta;
+                            for( j = 0; j < blockSize; j++ )
+                            {
+                                dstptr[j] = srcptr0[j]/srcptr1[j];
+                            }
+                            srcptr0 = (const float*)dstptr;
+                        }
+                    }
                     else if( op == MAX )
                     {
                         for( k = 1; k < n; k++ )
@@ -393,6 +408,11 @@ public:
                 for (int i = 2; i < inputs.size(); ++i)
                     multiply(inputs[i], outputs[0], outputs[0]);
                 break;
+            case DIV:
+                divide(inputs[0], inputs[1], outputs[0]);
+                for (int i = 2; i < inputs.size(); ++i)
+                    divide(outputs[0], inputs[i], outputs[0]);
+                break;
             case MAX:
                 max(inputs[0], inputs[1], outputs[0]);
                 for (int i = 2; i < inputs.size(); ++i)
@@ -486,6 +506,8 @@ public:
             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::SUM);
         else if (op == PROD)
             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MUL);
+        else if (op == DIV)
+            ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::DIV);
         else if (op == MAX)
             ieLayer.setEltwiseType(InferenceEngine::Builder::EltwiseLayer::EltwiseType::MAX);
         else
diff --git a/modules/dnn/src/onnx/onnx_importer.cpp b/modules/dnn/src/onnx/onnx_importer.cpp
index 51f9affe3b..ed7474f9db 100644
--- a/modules/dnn/src/onnx/onnx_importer.cpp
+++ b/modules/dnn/src/onnx/onnx_importer.cpp
@@ -520,19 +520,27 @@ void ONNXImporter::populateNet(Net dstNet)
         }
         else if (layer_type == "Div")
         {
-            Mat blob = getBlob(node_proto, constBlobs, 1);
-            CV_Assert_N(blob.type() == CV_32F, blob.total());
-            if (blob.total() == 1)
+            if (constBlobs.find(node_proto.input(1)) == constBlobs.end())
             {
-                layerParams.set("scale", 1.0f / blob.at<float>(0));
-                layerParams.type = "Power";
+                layerParams.type = "Eltwise";
+                layerParams.set("operation", "div");
             }
             else
             {
-                layerParams.type = "Scale";
-                divide(1.0, blob, blob);
-                layerParams.blobs.push_back(blob);
-                layerParams.set("bias_term", false);
+                Mat blob = getBlob(node_proto, constBlobs, 1);
+                CV_Assert_N(blob.type() == CV_32F, blob.total());
+                if (blob.total() == 1)
+                {
+                    layerParams.set("scale", 1.0f / blob.at<float>(0));
+                    layerParams.type = "Power";
+                }
+                else
+                {
+                    layerParams.type = "Scale";
+                    divide(1.0, blob, blob);
+                    layerParams.blobs.push_back(blob);
+                    layerParams.set("bias_term", false);
+                }
             }
         }
         else if (layer_type == "Neg")
@@ -771,6 +779,32 @@ void ONNXImporter::populateNet(Net dstNet)
                 continue;
             }
         }
+        else if (layer_type == "ReduceL2")
+        {
+            CV_Assert_N(node_proto.input_size() == 1, layerParams.has("axes"));
+            CV_Assert(graph_proto.node_size() > li + 1 && graph_proto.node(li + 1).op_type() == "Div");
+            ++li;
+            layerParams.type = "Normalize";
+
+            DictValue axes_dict = layerParams.get("axes");
+            if (axes_dict.size() != 1)
+                CV_Error(Error::StsNotImplemented, "Multidimensional reduceL2");
+            int axis = axes_dict.getIntValue(0);
+            layerParams.set("axis",axis);
+            layerParams.set("end_axis", axis);
+        }
+        else if (layer_type == "Squeeze")
+        {
+            CV_Assert_N(node_proto.input_size() == 1, layerParams.has("axes"));
+            DictValue axes_dict = layerParams.get("axes");
+            if (axes_dict.size() != 1)
+                CV_Error(Error::StsNotImplemented, "Multidimensional squeeze");
+
+            int axis = axes_dict.getIntValue(0);
+            layerParams.set("axis", axis - 1);
+            layerParams.set("end_axis", axis);
+            layerParams.type = "Flatten";
+        }
         else if (layer_type == "Unsqueeze")
         {
             CV_Assert(node_proto.input_size() == 1);
diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index f54ce77d59..b96f408f26 100644
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -318,6 +318,28 @@ TEST_P(Test_ONNX_layers, MultyInputs)
     expectNoFallbacksFromIE(net);
 }
 
+TEST_P(Test_ONNX_layers, Div)
+{
+    const String model =  _tf("models/div.onnx");
+    Net net = readNetFromONNX(model);
+    ASSERT_FALSE(net.empty());
+
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+
+    Mat inp1 = blobFromNPY(_tf("data/input_div_0.npy"));
+    Mat inp2 = blobFromNPY(_tf("data/input_div_1.npy"));
+    Mat ref  = blobFromNPY(_tf("data/output_div.npy"));
+    checkBackend(&inp1, &ref);
+
+    net.setInput(inp1, "0");
+    net.setInput(inp2, "1");
+    Mat out = net.forward();
+
+    normAssert(ref, out, "", default_l1,  default_lInf);
+    expectNoFallbacksFromIE(net);
+}
+
 TEST_P(Test_ONNX_layers, DynamicReshape)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
@@ -333,6 +355,16 @@ TEST_P(Test_ONNX_layers, Reshape)
     testONNXModels("unsqueeze");
 }
 
+TEST_P(Test_ONNX_layers, Squeeze)
+{
+    testONNXModels("squeeze");
+}
+
+TEST_P(Test_ONNX_layers, ReduceL2)
+{
+    testONNXModels("reduceL2");
+}
+
 TEST_P(Test_ONNX_layers, Slice)
 {
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2019010000)