# ADNet **Repository Path**: xxxxcp/ADNet ## Basic Information - **Project Name**: ADNet - **Description**: Attention-guided CNN for image denoising(Neural Networks,2020) - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-12-15 - **Last Updated**: 2021-12-15 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ## Atention-guided CNN for image denoising（ADNet）by Chunwei Tian, Yong Xu, Zuoyong Li, Wangmeng Zuo, Lunke Fei and Hong Liu is publised by Neural Networks (IF:8.05), 2020 (https://www.sciencedirect.com/science/article/pii/S0893608019304241) and it is implemented by Pytorch. ## This paper is pushed on home page of the Nueral Networks. Also, it is reported by wechat public accounts at https://mp.weixin.qq.com/s/Debh7PZSFTBtOVxpFh9yfQ and https://wx.zsxq.com/mweb/views/topicdetail/topicdetail.html?topic_id=548112815452544&group_id=142181451122&user_id=28514284588581&from=timeline. ## This paper is the first paper via deep network properties for addressing image denoising with complex background. ## Absract #### Deep convolutional neural networks (CNNs) have attracted considerable interest in low-level computer vision. Researches are usually devoted to improving the performance via very deep CNNs. However, as the depth increases, influences of the shallow layers on deep layers are weakened. Inspired by the fact, we propose an attention-guided denoising convolutional neural network (ADNet), mainly including a sparse block (SB), a feature enhancement block (FEB), an attention block (AB) and a reconstruction block (RB) for image denoising. Specifically, the SB makes a tradeoff between performance and efficiency by using dilated and common convolutions to remove the noise. The FEB integrates global and local features information via a long path to enhance the expressive ability of the denoising model. The AB is used to finely extract the noise information hidden in the complex background, which is very effective for complex noisy images, especially real noisy images and bind denoising. Also, the FEB is integrated with the AB to improve the efficiency and reduce the complexity for training a denoising model. Finally, a RB aims to construct the clean image through the obtained noise mapping and the given noisy image. Additionally, comprehensive experiments show that the proposed ADNet performs very well in three tasks (i.e., synthetic and real noisy images, and blind denoising) in terms of both quantitative and qualitative evaluations. The code of ADNet is accessible at https://github.com/hellloxiaotian/ADNet. ## Requirements (Pytorch) #### Pytorch 0.41 #### Python 2.7 #### torchvision #### openCv for Python #### HDF5 for Python ## Commands ### Training ## Commands ### Training ### Training datasets #### The training dataset of the gray noisy images is downloaded at https://pan.baidu.com/s/1nkY-b5_mdzliL7Y7N9JQRQ or https://drive.google.com/open?id=1_miSC9_luoUHSqMG83kqrwYjNoEus6Bj (google drive) #### The training dataset of the color noisy images is downloaded at https://pan.baidu.com/s/1ou2mK5JUh-K8iMu8-DMcMw (baiduyun) or https://drive.google.com/open?id=1S1_QrP-fIXeFl5hYY193lr07KyZV8X8r (google drive) #### Test dataset of Set68 is downloaded at https://drive.google.com/file/d/1_fw6EKne--LVnW0mo68RrIY-j6BKPdSp/view?usp=sharing (google drive) #### Test dataset of Set12 is downloaded at https://drive.google.com/file/d/1cpQwFpNv1MXsM5bJkIumYfww8EPtlkWf/view?usp=sharing (google drive) #### Test dataset of CBSD68 is downloaded at https://drive.google.com/file/d/1lxXQ_buMll_JVWxKpk5fp0jduW5F_MHe/view?usp=sharing (google drive) #### Test dataset of Kodak24 is downloaded at https://drive.google.com/file/d/1F4_mv4oTXhiG-zyG9DI4OO05KqvEKhs9/view?usp=sharing (google drive) #### The training dataset of real noisy images is downloaded at https://drive.google.com/file/d/1IYkR4zi76p7O5OCevC11VaQeKx0r1GyT/view?usp=sharing and https://drive.google.com/file/d/19MA-Rgfc89sW9GJHpj_QedFyo-uoS8o7/view?usp=sharing （google drive） #### The test dataset of real noisy images is downloaded at https://drive.google.com/file/d/17DE-SV85Slu2foC0F0Ftob5VmRrHWI2h/view?usp=sharing (google drive) ### Train ADNet-S (ADNet with known noise level) #### python train.py --prepropcess True --num_of_layers 17 --mode S --noiseL 25 --val_noiseL 25 ### Train ADNet-B (DnCNN with blind noise level) #### python train.py --preprocess True --num_of_layers 17 --mode B --val_noiseL 25 ### Test ### Gray noisy images #### python test.py --num_of_layers 17 --logdir g15 --test_data Set68 --test_noiseL 15 ### Gray blind denoising #### python test_Gb.py --num_of_layers 17 --logdir gblind --test_data Set68 --test_noiseL 25 ### Color noisy images #### python test_c.py --num_of_layers 17 --logdir g15 --test_data Set68 --test_noiseL 15 ### Color blind denoising #### python test_c.py --num_of_layers 17 --logdir cblind --test_data Set68 --test_noiseL 15 ### Network architecture ![RUNOOB 图标](./networkandresult/1.png) ### Test Results #### 1. ADNet for BSD68 ![RUNOOB 图标](./networkandresult/2BSD.png) #### 2. ADNet for Set12 ![RUNOOB 图标](./networkandresult/3Set12.png) #### 3. ADNet for CBSD68, Kodak24 and McMaster ![RUNOOB 图标](./networkandresult/4color.png) #### 4. ADNet for CBSD68, Kodak24 and McMaster ![RUNOOB 图标](./networkandresult/5realnoisy.png) #### 5. Running time of ADNet for a noisy image of different sizes. ![RUNOOB 图标](./networkandresult/6ruungtime.png) #### 6. Complexity of ADNet ![RUNOOB 图标](./networkandresult/7complexity.png) #### 7. 9 real noisy images ![RUNOOB 图标](./networkandresult/8realnoisy.png) #### 8. 9 thermodynamic images from the proposed A ![RUNOOB 图标](./networkandresult/9ab.png) #### 9. Visual results of BSD68 ![RUNOOB 图标](./networkandresult/9gray.png) #### 10. Visual results of Set12 ![RUNOOB 图标](./networkandresult/10gray.png) #### 11. Visual results of Kodak24 ![RUNOOB 图标](./networkandresult/11.png) #### 12. Visual results of McMaster ![RUNOOB 图标](./networkandresult/12.png) ### If you cite this paper, please the following format: #### 1.Tian C, Xu Y, Li Z, et al. Attention-guided CNN for image denoising[J]. Neural Networks, 2020, 124,177-129. #### 2.@article{tian2020attention, #### title={Attention-guided CNN for image denoising}, #### author={Tian, Chunwei and Xu, Yong and Li, Zuoyong and Zuo, Wangmeng and Fei, Lunke and Liu, Hong}, #### journal={Neural Networks}, #### volume={124}, #### pages={177--129}, #### year={2020}, #### publisher={Elsevier} #### }