diff --git a/contrib/SuperResolution/README.md b/contrib/SuperResolution/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d19f1069de8858760db4d5989ea004bf2221a398
--- /dev/null
+++ b/contrib/SuperResolution/README.md
@@ -0,0 +1,155 @@
+# 基于MxVision的FSRCNN图像超分辨率
+
+## 介绍
+
+基于MindX SDK 2.0.1 mxVision开发图像超分辨率程序。本程序采用python开发，通过预处理操作对输入的图片数据解码为YUV格式图片，并将解码后的图片缩放到模型推理要求的尺寸。然后利用图像超分辨率模型FSRCNN获取得到图片超分辨率重建结果。最后，利用python的第三方图像处理库PIL将低分辨率的输入图像和超分辨率重建的结果一同可视化。其次，针对两个图片集91-images和General-100进行PSNR（峰值信噪比）验证。
+
+程序输入：任意jpg图片
+程序输出：输出得到低分辨率图片（256 x 256px）和超分辨率重建图片（768 x 768px）组合的可视化大图
+
+## 目录结构
+
+```
+super_resolution
+|
+|-------- font
+|           |---- SourceHanSansCN-Normal-2.otf      // otf字体
+|-------- image
+|           |---- test.jpg                          // 测试图片(需自行准备)
+|-------- result                                    // 测试图片程序输出存放处
+|-------- model
+|           |---- YUV420SP_U8_GRAY.cfg              // 模型转换配置文件(灰度图)
+|           |---- model_conversion.sh               // 模型转换脚本
+|           |---- FSRCNN_256_256.om                 // 转换后OM模型
+|-------- testSet
+|           |---- 91-images-jpg                     // 91-images图片验证集
+|           |---- general-100-jpg                   // general-100图片验证集
+|           |---- output                            // 验证集结果输出目录
+|           |---- evaluate.py                       // 模型精度验证
+|-------- README.md                                 // ReadMe
+|-------- main.py                                   // 图像超分辨率主程序
+|-------- util.py                                   // 公共方法
+
+```
+
+## 准备工作
+
+> 模型转换
+
+**步骤1** 获取原始模型网络及权重, [权重下载地址](https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/super_resolution/FSRCNN/FSRCNN.caffemodel)、[网络下载地址](https://modelzoo-train-atc.obs.cn-north-4.myhuaweicloud.com/003_Atc_Models/AE/ATC%20Model/super_resolution/FSRCNN/FSRCNN.prototxt)
+
+**步骤2** AIPP配置文件-YUV420SP_U8_GRAY.cfg
+
+```cfg
+aipp_op {
+    aipp_mode: static
+    input_format : YUV420SP_U8
+    csc_switch : true
+    rbuv_swap_switch : false
+    matrix_r0c0 : 256
+    matrix_r0c1 : 0
+    matrix_r0c2 : 0
+    matrix_r1c0 : 0
+    matrix_r1c1 : 0
+    matrix_r1c2 : 0
+    matrix_r2c0 : 0
+    matrix_r2c1 : 0
+    matrix_r2c2 : 0
+    input_bias_0 : 0
+    input_bias_1 : 0
+    input_bias_2 : 0
+}
+```
+
+色域转换，用于将输入的图片格式，转换为模型需要的图片格式，这里将YUV420SP_U8转GRAY，详细色域转换配置说明请参考[这里](https://support.huawei.com/enterprise/zh/doc/EDOC1100191944/3e66a3c#ZH-CN_TOPIC_0000001095142890)
+
+**步骤3** 将下载得到模型网络及权重(`FSRCNN.prototxt`、`FSRCNN.caffemodel`)、AIPP配置文件放在 `model` 目录下
+
+**步骤4** 使用ATC模型转换工具进行模型转换
+
+设置环境变量（请确认install_path路径是否正确）
+
+```
+export install_path=/usr/local/Ascend/ascend-toolkit/latest
+export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
+export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg
+export LD_LIBRARY_PATH=${install_path}/atc/lib64:$LD_LIBRARY_PATH
+export ASCEND_OPP_PATH=${install_path}/opp
+```
+
+参照指令执行，转换FSRCNN模型成om格式
+
+```
+atc --model=./FSRCNN.prototxt --weight=./FSRCNN.caffemodel --framework=0 --input_format=NCHW --input_shape="data: 1, 1, 256, 256" --output=./FSRCNN_256_256 --soc_version=Ascend310 --output_type=FP32 --insert_op_conf=aYUV420SP_U8_GRAY.cfg
+```
+
+执行完模型转换脚本后，会生成相应的FSRCNN_256_256.om模型文件。
+
+模型转换使用了ATC工具，如需更多信息请参考 [这里]( https://support.huaweicloud.com/tg-cannApplicationDev330/atlasatc_16_0005.html)
+
+
+## 运行
+
+1) 配置环境变量
+
+```bash
+# 执行如下命令，打开.bashrc文件
+cd $HOME
+vi .bashrc
+
+# 在.bashrc文件中添加以下环境变量
+MX_SDK_HOME=${SDK安装路径}
+LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:/usr/local/Ascend/ascend-toolkit/latest/acllib/lib64:/usr/local/Ascend/driver/lib64/
+GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
+GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
+PYTHONPATH=${MX_SDK_HOME}/python
+
+# 保存退出.bashrc文件
+# 执行如下命令使环境变量生效
+source ~/.bashrc
+
+# 查看环境变量
+env
+```
+
+2) 准备一张室内图片，置于 image 文件夹中（仅支持jpg格式）
+
+2) 进入工程目录，键入执行指令，发起推理性能测试：
+
+```
+python3.7 main.py ${测试图片路径}
+例如: python3.7 main.py image/head.jpg
+```
+
+执行完毕后，sample会将程序输出保存在工程目录下`result`中 (文件名和输入图片一致)
+
+
+
+## PSNR验证
+
+对两个图片集91-images和General-100进行PSNR（峰值信噪比）测试
+
+具体命令文件参照如下目录
+
+```
+|-------- testSet
+|           |---- 91-images-jpg                     // 91-images图片验证集
+|           |---- general-100-jpg                   // general-100图片验证集
+|           |---- output                            // 验证集结果输出目录
+|           |---- evaluate.py                       // 模型精度验证
+```
+
+1. 91-images共计91张.bmp图片，General-100共计100张.bmp图片，使用PIL转换为jpg
+
+2. 将91-images图片输入模型中得到每个jpg图片的PSNR（峰值信噪比）如下
+   [33.64 28.86 29.10 28.22 27.60 27.53 27.47 27.43 28.58 27.75 29.77 28.10 28.42 27.47 28.20 27.68 29.88 27.99 32.31 32.64 28.06 27.63 32.66 33.02 30.46 27.62 28.72 30.91 28.53 28.06 27.58 27.38 27.50 27.55 30.04 29.70 29.80 28.23 27.56 29.73 27.48 27.59 27.95 32.77 28.92 29.88 29.60 27.59 30.77 29.94 28.18 32.10 30.69 30.27 35.63 28.35 29.41 27.06 30.12 28.46 29.52 29.01 27.51 29.46 29.70 30.51 27.43 27.53 27.51 28.82 28.06 29.58 28.79 29.93 28.83 28.17 33.54 28.89 27.84 29.64 31.87 30.58 27.54 32.66 27.78 31.56 31.25 28.09 29.24 28.76 28.17]
+   计算得到91张图片的平均PSNR avg= 29.23
+
+3. 将General-100图片输入模型中得到每个jpg图片的PSNR（峰值信噪比）如下
+   [29.09 30.25 28.06 29.66 28.70 29.06 28.23 31.02 30.34 28.04 27.58 28.36 29.46 27.87 27.59 29.78 31.77 29.70 29.56 28.91 29.76 27.70 28.85 27.83 28.43 29.49 28.66 29.53 27.87 30.36 27.57 27.54 34.45 28.10 28.91 28.47 28.62 27.75 29.82 29.00 27.53 26.83 28.26 28.36 28.26 28.19 28.18 28.98 29.44 28.51 27.73 27.93 28.91 28.81 28.50 28.43 28.42 27.32 28.87 29.33 30.58 29.31 29.69 28.45 30.17 33.51 27.67 29.88 27.38 30.73 33.13 31.50 31.45 27.64 28.48 29.98 29.43 28.78 30.10 30.90 27.77 31.66 27.11 29.68 32.94 30.61 28.45 33.29 27.73 30.12 28.11 27.74 29.48 29.42 28.97 28.16 29.49 29.28 28.88 29.56 ]
+   计算得到100张图片的平均PSNR avg= 29.16
+
+4. 可视化效果
+
+   ![head](./result/head.jpg)
+
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diff --git a/contrib/SuperResolution/main.py b/contrib/SuperResolution/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cc1d65bc70786495fde5b96a87d08c56a3e43d2
--- /dev/null
+++ b/contrib/SuperResolution/main.py
@@ -0,0 +1,166 @@
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+# Copyright(C) 2021. Huawei Technologies Co.,Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from StreamManagerApi import StreamManagerApi, MxDataInput, StringVector
+import MxpiDataType_pb2 as MxpiDataType
+from utils import colorize, calc_psnr
+import numpy as np
+from PIL import Image
+from PIL import ImageFont
+from PIL import ImageDraw
+
+
+if __name__ == '__main__':
+    # init stream manager
+    streamManagerApi = StreamManagerApi()
+    ret = streamManagerApi.InitManager()
+    if ret != 0:
+        print("Failed to init Stream manager, ret=%s" % str(ret))
+        exit()
+    # create streams by pipeline config file
+    pipeline = {
+        "superResolution": {
+            "stream_config": {
+                "deviceId": "0"
+            },
+            "appsrc0": {
+                "props": {
+                    "blocksize": "409600"
+                },
+                "factory": "appsrc",
+                "next": "mxpi_imagedecoder0"
+            },
+            "mxpi_imagedecoder0": {
+                "factory": "mxpi_imagedecoder",
+                "next": "mxpi_imageresize0"
+            },
+            "mxpi_imageresize0": {
+                "props": {
+                    "dataSource": "mxpi_imagedecoder0",
+                    "resizeHeight": "256",
+                    "resizeWidth": "256"
+                },
+                "factory": "mxpi_imageresize",
+                "next": "mxpi_tensorinfer0"
+            },
+            "mxpi_tensorinfer0": {
+                "props": {
+                    "dataSource": "mxpi_imagedecoder0",
+                    "modelPath": "model/FSRCNN_256_256.om"
+                },
+                "factory": "mxpi_tensorinfer",
+                "next": "appsink0"
+            },
+            "appsink0": {
+                "props": {
+                    "blocksize": "409600"
+                },
+                "factory": "appsink"
+            }
+        }
+    }
+    pipelineStr = json.dumps(pipeline).encode()
+    ret = streamManagerApi.CreateMultipleStreams(pipelineStr)
+    if ret != 0:
+        print("Failed to create Stream, ret=%s" % str(ret))
+        exit()
+
+    input_image_path = "./image/head.jpg"
+    if os.path.exists(input_image_path) != 1:
+        print("The image image does not exist.")
+
+    image = Image.open(input_image_path).convert('RGB')
+    # 768 x 768 high resolution image and 3x reduced image
+    hr = image.resize((768, 768), resample=Image.BICUBIC)
+    lr = hr.resize((256, 256), resample=Image.BILINEAR)
+    lr.save("./result/lr.jpg")
+
+    # construct the input of the stream
+    dataInput = MxDataInput()
+    with open("./result/lr.jpg", 'rb') as f:
+        dataInput.data = f.read()
+        os.remove("./result/lr.jpg")
+    streamName = b'superResolution'
+    inPluginId = 0
+    key = b"mxpi_tensorinfer0"
+    uniqueId = streamManagerApi.SendData(streamName, inPluginId, dataInput)
+    if uniqueId < 0:
+        print("Failed to send data to stream.")
+        exit()
+    keys = [b"mxpi_imagedecoder0", b"mxpi_tensorinfer0"]
+    keyVec = StringVector()
+    for key in keys:
+        keyVec.push_back(key)
+
+    inferResult = streamManagerApi.GetProtobuf(streamName, 0, keyVec)
+    if inferResult.size() == 0:
+        print("inferResult is null")
+        exit()
+    if inferResult[0].errorCode != 0:
+        print("GetProtobuf error. errorCode=%d, errorMsg=%s" % (
+            inferResult[0].errorCode, inferResult[0].messageName.decode()))
+        exit()
+    # get the infer result
+    inferList0 = MxpiDataType.MxpiTensorPackageList()
+    inferList0.ParseFromString(inferResult[1].messageBuf)
+    inferVisionData = inferList0.tensorPackageVec[0].tensorVec[0].dataStr
+
+    inferTensorShape = inferList0.tensorPackageVec[0].tensorVec[0].tensorShape
+    output_pic_data = np.frombuffer(inferVisionData, dtype="<f4")
+    output_y = colorize(output_pic_data, value_min=None, value_max=None)
+    output_y = output_y.reshape(inferTensorShape[2], inferTensorShape[3])
+    out_img_y = Image.fromarray(np.uint8(output_y), mode="L")
+
+    hr_YCbCr = hr.convert("YCbCr")
+    old_y, cb, cr = hr_YCbCr.split()
+    out_img = Image.merge("YCbCr", [out_img_y, cb, cr]).convert("RGB")
+
+    # calculate peak signal-to-noise ratio
+    PSNR = calc_psnr(out_img_y, old_y)
+    print('PSNR: {:.2f}'.format(PSNR))
+
+    # create canvas for finished drawing
+    target = Image.new('RGB', (lr.width + 5 + out_img.width, out_img.height + 30), "white")
+    # splice the pictures line by line
+    target.paste(lr, (0, out_img.height - lr.height))
+    target.paste(out_img, (lr.width + 5, 0))
+    font_set = {
+        "type": "./font/SourceHanSansCN-Normal-2.otf",
+        "size": 16,
+        "color": (0, 0, 0),
+        "psnr_content": 'PSNR: {:.2f}'.format(PSNR),
+        "origin_content": '输入图像',
+        "infer_content": '输出图像',
+        "psnr_location": (100, out_img.height - lr.height - 30),
+        "origin_location": (lr.width // 2 - 50, out_img.height + 10),
+        "infer_location": (lr.width * 2 + 100, out_img.height + 10),
+    }
+    # create a brush to write text to the picture
+    draw = ImageDraw.Draw(target)
+    # set font type and size
+    font = ImageFont.truetype(font_set["type"], font_set["size"])
+    # draw into the picture according to the position, content, color and font
+    draw.text(font_set["psnr_location"], font_set["psnr_content"], font_set["color"], font=font)
+    draw.text(font_set["origin_location"], font_set["origin_content"], font_set["color"], font=font)
+    draw.text(font_set["infer_location"], font_set["infer_content"], font_set["color"], font=font)
+    # save visualization results
+    _, fileName = os.path.split(input_image_path)
+    out_path = "./result/" + fileName
+    target.save(out_path, quality=50)
+    # destroy streams
+    streamManagerApi.DestroyAllStreams()
\ No newline at end of file
diff --git a/contrib/SuperResolution/model/FSRCNN_256_256.om b/contrib/SuperResolution/model/FSRCNN_256_256.om
new file mode 100644
index 0000000000000000000000000000000000000000..d2cdc74713eeff5cb8c24b0d78c1838deb11f04e
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diff --git a/contrib/SuperResolution/model/YUV420SP_U8_GRAY.cfg b/contrib/SuperResolution/model/YUV420SP_U8_GRAY.cfg
new file mode 100644
index 0000000000000000000000000000000000000000..be0f13d6e6dbf170a0da43356398874eaed89146
--- /dev/null
+++ b/contrib/SuperResolution/model/YUV420SP_U8_GRAY.cfg
@@ -0,0 +1,18 @@
+aipp_op {
+    aipp_mode: static
+    input_format : YUV420SP_U8
+    csc_switch : true
+    rbuv_swap_switch : false
+    matrix_r0c0 : 256
+    matrix_r0c1 : 0
+    matrix_r0c2 : 0
+    matrix_r1c0 : 0
+    matrix_r1c1 : 0
+    matrix_r1c2 : 0
+    matrix_r2c0 : 0
+    matrix_r2c1 : 0
+    matrix_r2c2 : 0
+    input_bias_0 : 0
+    input_bias_1 : 0
+    input_bias_2 : 0
+}
\ No newline at end of file
diff --git a/contrib/SuperResolution/model/model_conversion.sh b/contrib/SuperResolution/model/model_conversion.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f6f39d3cb2fd956e86871b8439f2e803af9b943c
--- /dev/null
+++ b/contrib/SuperResolution/model/model_conversion.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+# Copyright(C) 2021. Huawei Technologies Co.,Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+set -e
+
+# 该脚本用来将onnx模型文件转换成.om模型文件
+# This is used to convert onnx model file to .om model file.
+
+
+# 设置环境变量（请确认install_path路径是否正确）
+# Set environment PATH (Please confirm that the install_path is correct).
+
+export install_path=/usr/local/Ascend/ascend-toolkit/latest
+export PATH=/usr/local/python3.7.5/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
+export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg
+export LD_LIBRARY_PATH=${install_path}/atc/lib64:$LD_LIBRARY_PATH
+export ASCEND_OPP_PATH=${install_path}/opp
+
+
+# 执行，转换Adabins_nyu模型
+# Execute, transform Adabins_nyu model.
+
+atc --model=./FSRCNN.prototxt --weight=./FSRCNN.caffemodel --framework=0 --input_format=NCHW --input_shape="data: 1, 1, 256, 256" --output=./FSRCNN_256_256 --soc_version=Ascend310 --output_type=FP32 --insert_op_conf=aYUV420SP_U8_GRAY.cfg
+# 退出
+# exit
+exit 0
\ No newline at end of file
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diff --git a/contrib/SuperResolution/testSet/evaluate.py b/contrib/SuperResolution/testSet/evaluate.py
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index 0000000000000000000000000000000000000000..5e891819dec1af31b4112c43b1a54022e21e0d67
--- /dev/null
+++ b/contrib/SuperResolution/testSet/evaluate.py
@@ -0,0 +1,183 @@
+# !/usr/bin/env python
+# -*- encoding: utf-8 -*-
+# Copyright(C) 2021. Huawei Technologies Co.,Ltd. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from StreamManagerApi import StreamManagerApi, MxDataInput, StringVector
+import MxpiDataType_pb2 as MxpiDataType
+from utils import colorize, calc_psnr
+import numpy as np
+from PIL import Image
+from PIL import ImageFont
+from PIL import ImageDraw
+
+
+def infer(inner_img, streamManagerapi, resample__):
+    streamName = b'superResolution'
+    inPluginId = 0
+    key = b"mxpi_tensorinfer0"
+
+    # construct the input of the stream
+    hr_img_path = inner_img
+    if os.path.exists(hr_img_path) != 1:
+        print("The image image does not exist.")
+    image = Image.open(hr_img_path).convert('RGB')
+
+    # 768 x 768 high resolution image and 3x reduced image
+    hr = image.resize((768, 768), resample=Image.BICUBIC)
+    lr = hr.resize((256, 256), resample=resample__)
+    lr.save("./output/lr.jpg")
+
+    dataInput = MxDataInput()
+    with open("./output/lr.jpg", 'rb') as f:
+        dataInput.data = f.read()
+        os.remove("./output/lr.jpg")
+
+    uniqueId = streamManagerapi.SendData(streamName, inPluginId, dataInput)
+    if uniqueId < 0:
+        print("Failed to send data to stream.")
+        exit()
+    keys = [b"mxpi_imagedecoder0", b"mxpi_tensorinfer0"]
+    keyVec = StringVector()
+    for key in keys:
+        keyVec.push_back(key)
+
+    inferResult = streamManagerapi.GetProtobuf(streamName, 0, keyVec)
+    if inferResult.size() == 0:
+        print("inferResult is null")
+        exit()
+    if inferResult[0].errorCode != 0:
+        print("GetProtobuf error. errorCode=%d, errorMsg=%s" % (
+            inferResult[0].errorCode, inferResult[0].messageName.decode()))
+        exit()
+    # get the infer result
+    inferList0 = MxpiDataType.MxpiTensorPackageList()
+    inferList0.ParseFromString(inferResult[1].messageBuf)
+    inferVisionData = inferList0.tensorPackageVec[0].tensorVec[0].dataStr
+
+    inferTensorShape = inferList0.tensorPackageVec[0].tensorVec[0].tensorShape
+    output_pic_data = np.frombuffer(inferVisionData, dtype="<f4")
+    output_y = colorize(output_pic_data, value_min=None, value_max=None)
+    output_y = output_y.reshape(inferTensorShape[2], inferTensorShape[3])
+    out_img_y = Image.fromarray(np.uint8(output_y), mode="L")
+
+    hr_YCbCr = hr.convert("YCbCr")
+    old_y, cb, cr = hr_YCbCr.split()
+    out_img = Image.merge("YCbCr", [out_img_y, cb, cr]).convert("RGB")
+
+    # calculate peak signal-to-noise ratio
+    PSNR = calc_psnr(out_img_y, old_y)
+    res_all.append(PSNR)
+    print('PSNR: {:.2f}'.format(PSNR))
+
+    # create canvas for finished drawing
+    target = Image.new('RGB', (lr.width + 5 + out_img.width, out_img.height + 30), "white")
+    # splice the pictures line by line
+    target.paste(lr, (0, out_img.height - lr.height))
+    target.paste(out_img, (lr.width + 5, 0))
+    font_set = {
+        "type": "../font/SourceHanSansCN-Normal-2.otf",
+        "size": 16,
+        "color": (0, 0, 0),
+        "psnr_content": 'PSNR: {:.2f}'.format(PSNR),
+        "origin_content": '输入图像',
+        "infer_content": '输出图像',
+        "psnr_location": (100, out_img.height - lr.height - 30),
+        "origin_location": (lr.width // 2 - 50, out_img.height + 10),
+        "infer_location": (lr.width * 2 + 100, out_img.height + 10),
+    }
+    # create a brush to write text to the picture
+    draw = ImageDraw.Draw(target)
+    # set font type and size
+    font = ImageFont.truetype(font_set["type"], font_set["size"])
+    # draw into the picture according to the position, content, color and font
+    draw.text(font_set["psnr_location"], font_set["psnr_content"], font_set["color"], font=font)
+    draw.text(font_set["origin_location"], font_set["origin_content"], font_set["color"], font=font)
+    draw.text(font_set["infer_location"], font_set["infer_content"], font_set["color"], font=font)
+    # save visualization results
+    _, fileName = os.path.split(inner_img)
+    out_path = "./output/" + fileName
+    target.save(out_path, quality=50)
+
+
+if __name__ == '__main__':
+    # init stream manager
+    streamManagerApi = StreamManagerApi()
+    ret = streamManagerApi.InitManager()
+    if ret != 0:
+        print("Failed to init Stream manager, ret=%s" % str(ret))
+        exit()
+    # create streams by pipeline config file
+    pipeline = {
+        "superResolution": {
+            "stream_config": {
+                "deviceId": "0"
+            },
+            "appsrc0": {
+                "props": {
+                    "blocksize": "409600"
+                },
+                "factory": "appsrc",
+                "next": "mxpi_imagedecoder0"
+            },
+            "mxpi_imagedecoder0": {
+                "factory": "mxpi_imagedecoder",
+                "next": "mxpi_imageresize0"
+            },
+            "mxpi_imageresize0": {
+                "props": {
+                    "dataSource": "mxpi_imagedecoder0",
+                    "resizeHeight": "256",
+                    "resizeWidth": "256"
+                },
+                "factory": "mxpi_imageresize",
+                "next": "mxpi_tensorinfer0"
+            },
+            "mxpi_tensorinfer0": {
+                "props": {
+                    "dataSource": "mxpi_imagedecoder0",
+                    "modelPath": "../model/FSRCNN_256_256.om"
+                },
+                "factory": "mxpi_tensorinfer",
+                "next": "appsink0"
+            },
+            "appsink0": {
+                "props": {
+                    "blocksize": "409600"
+                },
+                "factory": "appsink"
+            }
+        }
+    }
+    pipelineStr = json.dumps(pipeline).encode()
+    ret = streamManagerApi.CreateMultipleStreams(pipelineStr)
+    if ret != 0:
+        print("Failed to create Stream, ret=%s" % str(ret))
+        exit()
+    res_all = []
+    resample_ = Image.BILINEAR
+    # for i in range(1, 92, 1):
+    #     image_file = './91-images-jpg/t_'+str(i)+'.jpg'
+    #     infer(image_file, streamManagerApi, resample_)
+
+    for i in range(1, 101, 1):
+        image_file = './general-100-jpg/im_'+str(i)+'.jpg'
+        infer(image_file, streamManagerApi, resample_)
+
+    print(str(resample_) + " sum = " + str(sum(res_all)/len(res_all)))
+    print(res_all)
+    # destroy streams
+    streamManagerApi.DestroyAllStreams()
\ No newline at end of file
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diff --git a/contrib/SuperResolution/utils.py b/contrib/SuperResolution/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..263d1321f9aa7500db5207a506965bee1c8a8c0e
--- /dev/null
+++ b/contrib/SuperResolution/utils.py
@@ -0,0 +1,43 @@
+import numpy as np
+import math
+
+
+def colorize(value, value_min=10, value_max=1000, color_depth=255, extend_to_bgr=False, dominant_color=100):
+    # normalize
+    value_min = value.min() if value_min is None else value_min
+    value_max = value.max() if value_max is None else value_max
+    if value_min != value_max:
+        value = (value - value_min) / (value_max - value_min)
+    else:
+        # Avoid 0-division
+        value = value * 0.
+
+    value = np.clip((value * color_depth), 0, color_depth)
+    if extend_to_bgr:
+        use_blue = int(dominant_color / 100) == 1
+        dominant_color = dominant_color % 100
+        use_green = int(dominant_color / 10) == 1
+        dominant_color = dominant_color % 10
+        use_red = int(dominant_color / 1) == 1
+
+        img = np.zeros((value.shape[0], value.shape[1], 3))
+        if use_blue:
+            img[:, :, 0] = value
+        if use_green:
+            img[:, :, 1] = value
+        if use_red:
+            img[:, :, 2] = value
+
+        return img
+    else:
+        return value
+
+
+def calc_psnr(src_img, dst_img):
+    src_img = np.array(src_img).astype(np.uint8)
+    dst_img = np.array(dst_img).astype(np.uint8)
+    mse = np.mean((src_img - dst_img) ** 2)
+    if mse == 0:
+        return 100
+    PIXEL_MAX = 255.0
+    return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
diff --git a/contrib/build_all.sh b/contrib/build_all.sh
index 4144e9876f4b81f7c37a99bd413646ca3daaa9cc..5333d3adedced4c92a4ae968e9ea603b62ed6f9e 100644
--- a/contrib/build_all.sh
+++ b/contrib/build_all.sh
@@ -28,6 +28,7 @@ SAMPLE_FOLDER=(
 	OpenposeKeypointDetection/
 	PersonCount/
 	FatigueDrivingRecognition/
+	SuperResolution/
 )