diff --git a/delegate/opaque/src/Resize.hpp b/delegate/opaque/src/Resize.hpp
index e169697..509ae62 100644
--- a/delegate/opaque/src/Resize.hpp
+++ b/delegate/opaque/src/Resize.hpp
@@ -2,3 +2,221 @@
 // Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
+
+#pragma once
+
+#include <OpaqueDelegateUtils.hpp>
+
+namespace armnnOpaqueDelegate
+{
+
+TfLiteStatus ValidateResizeOperator(DelegateData& delegateData,
+                                    TfLiteOpaqueContext* tfLiteContext,
+                                    const armnn::TensorInfo& inputInfo,
+                                    const armnn::TensorInfo& outputInfo,
+                                    const armnn::ResizeDescriptor& descriptor)
+{
+    bool isSupported = false;
+    FORWARD_LAYER_OPAQUE_SUPPORT_FUNC("RESIZE",
+                                       tfLiteContext,
+                                       IsResizeSupported,
+                                       delegateData.m_Backends,
+                                       isSupported,
+                                       armnn::BackendId(),
+                                       inputInfo,
+                                       outputInfo,
+                                       descriptor);
+
+    return isSupported ? kTfLiteOk : kTfLiteError;
+}
+
+TfLiteStatus VisitResizeOperator(DelegateData& delegateData,
+                                 TfLiteOpaqueContext* tfLiteContext,
+                                 TfLiteOpaqueNode* tfLiteNode,
+                                 int nodeIndex,
+                                 int32_t resizeOperatorCode)
+{
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    // Gather input indices and use to get input tensor.
+    auto numInputs = TfLiteOpaqueNodeNumberOfInputs(tfLiteNode);
+    const int* inputTensors;
+    if (TfLiteOpaqueNodeInputs(tfLiteNode, &inputTensors, &numInputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather input tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    // The first input contains the data of the image that should be resized [batch, height, width, channels]
+    const TfLiteOpaqueTensor* tfLiteInputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[0]);
+    if (IsDynamicTensor(tfLiteInputTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+                resizeOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // The second input contains a size tensor. The size tensor contains two integer values
+    // that describe the new height and width of the image [new_height, new_width]
+    const TfLiteOpaqueTensor* tfLiteSizeTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, inputTensors[1]);
+    if (IsDynamicTensor(tfLiteSizeTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+                resizeOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Gather output indices and use to get output tensors.
+    int numOutputs = 0;
+    const int* outputTensors;
+    if (TfLiteOpaqueNodeOutputs(tfLiteNode, &outputTensors, &numOutputs) != kTfLiteOk)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Unable to gather output tensor indices from node #%d: ",
+                nodeIndex);
+        return kTfLiteError;
+    }
+
+    // The output tensor should have the shape [batch, new_height, new_width, channels]
+    const TfLiteOpaqueTensor* tfLiteOutputTensor =
+            TfLiteOpaqueContextGetOpaqueTensor(tfLiteContext, outputTensors[0]);
+    if (IsDynamicTensor(tfLiteOutputTensor))
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+                resizeOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo =
+            GetTensorInfoForTfLiteOpaqueTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& outputTensorInfo =
+            GetTensorInfoForTfLiteOpaqueTensor(tfLiteOutputTensor, true);
+
+    std::string layerName("Resize");
+
+    // Fill descriptor
+    armnn::ResizeDescriptor desc;
+    switch (resizeOperatorCode)
+    {
+        case kTfLiteBuiltinResizeBilinear:
+        {
+            desc.m_Method = armnn::ResizeMethod::Bilinear;
+
+            layerName += "Bilinear:" + std::to_string(nodeIndex);
+
+            TfLiteResizeBilinearParams* bilinearOptions =
+                    reinterpret_cast<TfLiteResizeBilinearParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
+
+            desc.m_AlignCorners = bilinearOptions->align_corners;
+            desc.m_HalfPixelCenters = bilinearOptions->half_pixel_centers;
+            break;
+        }
+        case kTfLiteBuiltinResizeNearestNeighbor:
+        {
+            desc.m_Method =  armnn::ResizeMethod::NearestNeighbor;
+            layerName += "NearestNeighbor:" + std::to_string(nodeIndex);
+
+            TfLiteResizeNearestNeighborParams* nearestNeighborOptions =
+                    reinterpret_cast<TfLiteResizeNearestNeighborParams*>(TfLiteOpaqueNodeGetBuiltinData(tfLiteNode));
+
+            desc.m_AlignCorners = nearestNeighborOptions->align_corners;
+            desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers;
+            break;
+        }
+        default:
+        {
+            TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnOpaqueDelegate: Unknown TfLite built in operation for Resize. "
+                    "Given operator: #%d node #%d: ",
+                    resizeOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+    }
+
+    // In Arm NN the values of the size input tensor [new_height, new_width] is saved in the operator
+    // descriptor. We have to read it from the input tensor and write it to the descriptor.
+
+    auto* sizeTensorDataPtr = static_cast<int*>(TfLiteOpaqueTensorData(tfLiteSizeTensor));
+    auto sizeTensorNumDimensions = TfLiteOpaqueTensorNumDims(tfLiteSizeTensor);
+    // The size tensor is only a 1D tensor -> [new_height, new width]
+    if (sizeTensorNumDimensions != 1)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
+                "dynamic tensor. Operator: #%d node #%d: ",
+                resizeOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    // Get number of values in the size tensor
+    auto sizeTensorNumValues = TfLiteOpaqueTensorDim(tfLiteSizeTensor,0);
+    if (sizeTensorNumValues == 0)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
+                "dynamic tensor. Operator: #%d node #%d: ",
+                resizeOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+    else if (sizeTensorNumValues != 2)
+    {
+        TF_LITE_OPAQUE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnOpaqueDelegate: The Size-Input-Tensor of the Resize operation requires to "
+                "have a dimension of 2 [new_height, new width] but a tensor with a dimension of #%d was given. "
+                "Operator: #%d node #%d: ",
+                sizeTensorNumValues, resizeOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+    // get size tensor data
+    std::vector<int32_t> sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues);
+
+    desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]);
+    desc.m_TargetWidth  = static_cast<uint32_t> (sizeTensorData[1]);
+    desc.m_DataLayout   = armnn::DataLayout::NHWC;
+
+    // No network pointer indicates that only support for this operator should be checked
+    if (!delegateData.m_Network)
+    {
+        return ValidateResizeOperator(delegateData,
+                                      tfLiteContext,
+                                      inputTensorInfo,
+                                      outputTensorInfo,
+                                      desc);
+    }
+
+
+    armnn::IConnectableLayer* resizeLayer = nullptr;
+    resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str());
+
+    armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // try to connect the Constant Inputs if there are any
+    if(ProcessInputs(resizeLayer,delegateData, tfLiteContext, tfLiteNode) != kTfLiteOk )
+    {
+        return kTfLiteError;
+    }
+
+    ARMNN_ASSERT(resizeLayer != nullptr);
+
+    return Connect(resizeLayer, tfLiteContext, tfLiteNode, delegateData);
+}
+
+} // namespace armnnOpaqueDelegate