diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/.keep b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/.keep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_postprocess.py b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_postprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbaf8635a6f9cb01c11193a030c60e08ed8b2147
--- /dev/null
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_postprocess.py
@@ -0,0 +1,103 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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 os
+import sys
+import glob
+import numpy as np
+import cv2
+import torch
+import torch.nn as nn
+import struct
+from skimage.metrics import peak_signal_noise_ratio as compare_psnr
+
+
+def batch_PSNR(img, imclean, data_range):
+
+    Img = img.data.cpu().numpy().astype(np.float32)
+    Iclean = imclean.data.cpu().numpy().astype(np.float32)
+    PSNR = 0
+    for i in range(Img.shape[0]):
+        PSNR += compare_psnr(Iclean[i, :, :, :], Img[i, :, :, :], data_range=data_range)
+    return (PSNR / Img.shape[0])
+
+
+def bin2npy(filepath):
+
+    size = os.path.getsize(filepath)  
+    res = []
+    L = int(size / 4)
+    binfile = open(filepath, 'rb')  
+    for i in range(L):
+        data = binfile.read(4)  
+        num = struct.unpack('f', data)
+        res.append(num[0])
+    binfile.close()
+    dim_res = np.array(res).reshape(1, 1, 481, 481)
+    return dim_res
+
+
+def main(Result_path):
+
+    # load data info
+    print('Loading ISource bin ...\n')
+    ISource = glob.glob(os.path.join('ISource', '*.bin'))
+    ISource.sort()
+    print('Loading INoisy bin ...\n')
+    INoisy = glob.glob(os.path.join('INoisy', '*.bin'))
+    INoisy.sort()
+    # load result file
+    print('Loading res bin ...\n')
+    Result_path = glob.glob(os.path.join(Result_path, '*.bin'))
+    Result_path.sort()
+
+    # begin data
+    print('begin infer')
+    psnr_test = 0
+    n_lables = 0
+
+    for isource in ISource:
+        isource_name = isource
+        # isource
+        isource = bin2npy(isource)
+        isource = torch.from_numpy(isource)
+        # inoisy
+        inoisy = bin2npy(INoisy[n_lables])
+        inoisy = torch.from_numpy(inoisy)
+        # Result_path
+        Result = bin2npy(Result_path[n_lables])
+        Result = torch.from_numpy(Result)
+        n_lables += 1
+        print('infering...')
+        with torch.no_grad(): 
+            Out = torch.clamp(inoisy - Result, 0., 1.)
+        psnr = batch_PSNR(Out, isource, 1.)
+        psnr_test += psnr
+        print("%s PSNR %f" % (isource_name, psnr))
+    psnr_test /= len(ISource)
+    print("\nPSNR on test data %f" % psnr_test)
+
+if __name__ == "__main__":
+    
+    try:
+        Result_path = sys.argv[1]
+
+    except IndexError:
+        print("Stopped!")
+        exit(1)
+
+    if not (os.path.exists(Result_path)):
+        print("Result path doesn't exist.")
+
+    main(Result_path)
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_preprocess.py b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd612dcef814aa1947311432bdab6c9102968092
--- /dev/null
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_preprocess.py
@@ -0,0 +1,79 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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 sys
+import os
+import os.path
+import numpy as np
+import random
+import torch
+import cv2
+import glob
+
+infer_data = 'Set68'
+infer_noiseL = 15
+
+def normalize(data):
+    return data / 255.
+
+
+def proprecess(data_path, ISource_bin, INoisy_bin):
+
+    # load data info
+    print('Loading data info ...\n')
+    files = glob.glob(os.path.join(data_path, infer_data, '*.png'))
+    files.sort()
+    # process data
+    for i in range(len(files)):
+        # image
+        filename = os.path.basename(files[i])
+        img = cv2.imread(files[i])
+        img = normalize(np.float32(img[:, :, 0]))
+
+        img_padded = np.full([481, 481], 0, dtype=np.float32)
+        width_offset = (481 - img.shape[1]) // 2
+        height_offset = (481 - img.shape[0]) // 2
+        img_padded[height_offset:height_offset + img.shape[0], width_offset:width_offset + img.shape[1]] = img
+        img = img_padded
+
+        img = np.expand_dims(img, 0)
+        img = np.expand_dims(img, 1)
+
+        ISource = torch.Tensor(img)
+        # noise
+        noise = torch.FloatTensor(ISource.size()).normal_(mean=0, std=infer_noiseL / 255.)
+        # noisy image
+        INoisy = ISource + noise
+
+        # save ISource_bin
+        ISource = ISource.numpy()
+        print("ISource shape is", ISource.shape)
+        ISource.tofile(os.path.join(ISource_bin, filename.split('.')[0] + '.bin'))
+        
+        # save INoisy_bin
+        INoisy = INoisy.numpy()
+        print("INoisy shape is", INoisy.shape) 
+        INoisy.tofile(os.path.join(INoisy_bin, filename.split('.')[0] + '.bin'))
+        
+if __name__ == '__main__':
+    
+    data_path = sys.argv[1]
+    ISource_bin =  sys.argv[2]
+    INoisy_bin = sys.argv[3]
+    if os.path.exists(ISource_bin) is False:
+        os.mkdir(ISource_bin)
+    if os.path.exists(INoisy_bin) is False:
+        os.mkdir(INoisy_bin)
+
+    proprecess(data_path, ISource_bin, INoisy_bin)
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_pth2onnx.py b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_pth2onnx.py
new file mode 100644
index 0000000000000000000000000000000000000000..8362e6eea7735e96a7763fd570a6553104eb0a7d
--- /dev/null
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/DnCNN_pth2onnx.py
@@ -0,0 +1,80 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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 torch
+import torch.onnx
+import torch.nn as nn
+import sys
+
+from collections import OrderedDict
+
+class DnCNN(nn.Module):
+    def __init__(self, channels, num_of_layers=17):
+        super(DnCNN, self).__init__()
+        kernel_size = 3
+        padding = 1
+        features = 64
+        layers = []
+        layers.append(nn.Conv2d(in_channels=channels, out_channels=features, \
+                      kernel_size=kernel_size, padding=padding, bias=False))
+        layers.append(nn.ReLU(inplace=True))
+        for _ in range(num_of_layers - 2):
+            layers.append(nn.Conv2d(in_channels=features, out_channels=features, \
+                          kernel_size=kernel_size, padding=padding, bias=False))
+            layers.append(nn.BatchNorm2d(features))
+            layers.append(nn.ReLU(inplace=True))
+        layers.append(nn.Conv2d(in_channels=features, out_channels=channels, \
+                      kernel_size=kernel_size, padding=padding, bias=False))
+        self.dncnn = nn.Sequential(*layers)
+
+    def forward(self, x):
+    
+        out = self.dncnn(x)
+        return out
+
+
+def proc_nodes_module(checkpoint):
+
+    new_state_dict = OrderedDict()
+    for k, v in checkpoint.items():
+        if(k[0:7] == "module."):
+            name = k[7:]
+        else:
+            name = k[0:]
+        new_state_dict[name]=v
+    return new_state_dict
+
+
+def convert(pth_file, onnx_file):
+
+    pretrained_net = torch.load(pth_file, map_location='cpu')
+    pretrained_net['state_dict'] = proc_nodes_module(pretrained_net)
+
+    model = DnCNN(channels=1, num_of_layers=17)
+    model.load_state_dict(pretrained_net['state_dict'])
+    model.eval()
+    input_names = ["actual_input_1"]
+    dummy_input = torch.randn(1, 1, 481, 481)
+    #torch.onnx.export(model, dummy_input, onnx_file, input_names = input_names, opset_version=11, verbose=True)
+    dynamic_axes = {'actual_input_1': {0: '-1'}}
+    torch.onnx.export(model, dummy_input, onnx_file, dynamic_axes=dynamic_axes, \
+                      input_names=input_names, opset_version=11)
+
+if __name__ == "__main__":
+    
+    pth_file = sys.argv[1]
+    onnx_file = sys.argv[2]
+
+    convert(pth_file, onnx_file)
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/get_info.py b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/get_info.py
new file mode 100644
index 0000000000000000000000000000000000000000..def864bec0be6d2a594819936f282ea03aba46f4
--- /dev/null
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/get_info.py
@@ -0,0 +1,60 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# 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 os
+import sys
+import cv2
+from glob import glob
+
+
+def get_bin_info(file_path, info_name, width, height):
+    bin_images = glob(os.path.join(file_path, '*.bin'))
+    with open(info_name, 'w') as file:
+        for index, img in enumerate(bin_images):
+            content = ' '.join([str(index), img, width, height])
+            file.write(content)
+            file.write('\n')
+
+
+def get_jpg_info(file_path, info_name):
+    extensions = ['jpg', 'jpeg', 'JPG', 'JPEG']
+    image_names = []
+    for extension in extensions:
+        image_names.append(glob(os.path.join(file_path, '*.' + extension)))  
+    with open(info_name, 'w') as file:
+        for image_name in image_names:
+            if len(image_name) == 0:
+                continue
+            else:
+                for index, img in enumerate(image_name):
+                    img_cv = cv2.imread(img)
+                    shape = img_cv.shape
+                    width, height = shape[1], shape[0]
+                    content = ' '.join([str(index), img, str(width), str(height)])
+                    file.write(content)
+                    file.write('\n')
+
+
+if __name__ == '__main__':
+    file_type = sys.argv[1]
+    file_path = sys.argv[2]
+    info_name = sys.argv[3]
+    if file_type == 'bin':
+        width = sys.argv[4]
+        height = sys.argv[5]
+        assert len(sys.argv) == 6, 'The number of input parameters must be equal to 5'
+        get_bin_info(file_path, info_name, width, height)
+    elif file_type == 'jpg':
+        assert len(sys.argv) == 4, 'The number of input parameters must be equal to 3'
+        get_jpg_info(file_path, info_name)
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/requirements.txt b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..965bdf8bdd73d4bbaab48e2fcc37f0cab79f100d
--- /dev/null
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/DnCNN_710/requirements.txt
@@ -0,0 +1,6 @@
+torch==1.8.0
+torchvision==0.9.0
+onnx==1.9.0
+numpy==1.20.2
+opencv-python==4.5.2.52
+scikit-image==0.16.2
\ No newline at end of file
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/READEME.md b/ACL_PyTorch/contrib/cv/image_process/DnCNN/READEME.md
index 592f7f9936ff3c5bcb93ad47f60fcb136bf32842..35ee9aa42b639f6d9bb55976fe5e294f92692d83 100644
--- a/ACL_PyTorch/contrib/cv/image_process/DnCNN/READEME.md
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/READEME.md
@@ -108,10 +108,22 @@ python3.7 DnCNN_pth2onnx.py net.pth DnCNN-S-15.onnx
 ```
 source env.sh
 ```
-2.使用atc将onnx模型转换为om模型文件
+2.增加benchmark.{arch}可执行权限。
+```
+chmod u+x benchmark.x86_64
+```
+3.使用atc将onnx模型转换为om模型文件
 ```
 atc --framework=5 --model=./DnCNN-S-15.onnx --input_format=NCHW --input_shape="actual_input_1:1,1,481,481" --output=DnCNN-S-15_bs1 --log=debug --soc_version=Ascend310
 ```
+(710_bs1)
+```
+atc --framework=5 --model=./DnCNN-S-15.onnx --input_format=NCHW --input_shape="actual_input_1:1,1,481,481" --output=DnCNN-S-15_bs1 --log=debug --soc_version=Ascend710
+```
+(710_bs16)
+```
+atc --framework=5 --model=./DnCNN-S-15.onnx --input_format=NCHW --input_shape="actual_input_1:16,1,481,481" --output=DnCNN-S-15_bs16 --log=debug --soc_version=Ascend710
+```
 
 ## 4 数据集预处理
 
@@ -155,9 +167,15 @@ benchmark工具为华为自研的模型推理工具，支持多种模型的离
 source env.sh
 ```
 2.执行离线推理
+(bs1)
+```
+./benchmark.x86_64 -model_type=vision -om_path=DnCNN-S-15_bs1.om -device_id=0 -batch_size=1 -input_text_path=DnCNN_bin.info -input_width=481 -input_height=481 -useDvpp=false -output_binary=true
 ```
-./benchmark.x86_64 -model_type=vision -om_path=DnCNN-S-15.om -device_id=0 -batch_size=1 -input_text_path=DnCNN_bin.info -input_width=481 -input_height=481 -useDvpp=false -output_binary=true
+(bs16)
 ```
+./benchmark.x86_64 -model_type=vision -om_path=DnCNN-S-15_bs16.om -device_id=0 -batch_size=16 -input_text_path=DnCNN_bin.info -input_width=481 -input_height=481 -useDvpp=false -output_binary=true
+```
+
 输出结果默认保存在当前目录result/dumpOutput_deviceX(X为对应的device_id)，每个输入对应的输出对应一个_X.bin文件。
 
 ## 6 精度对比
diff --git a/ACL_PyTorch/contrib/cv/image_process/DnCNN/postprocess.py b/ACL_PyTorch/contrib/cv/image_process/DnCNN/postprocess.py
index 0333302e810f7ded587c87489659fdf8a766484e..7945fd671a25c6e800b5ef1b4631722d09bce708 100644
--- a/ACL_PyTorch/contrib/cv/image_process/DnCNN/postprocess.py
+++ b/ACL_PyTorch/contrib/cv/image_process/DnCNN/postprocess.py
@@ -20,7 +20,7 @@ import cv2
 import torch
 import torch.nn as nn
 import struct
-from skimage.measure.simple_metrics import compare_psnr
+from skimage.metrics import peak_signal_noise_ratio as compare_psnr
 
 
 def batch_PSNR(img, imclean, data_range):