|
|
|
| Single Image Rain Removal Method by Fusing Residual and Channel Attention Mechanism |
| ZHANG Shi-hui1,2,YAN Xiao-rui1,SANG Yu1 |
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract In order to remove the raindrops and restore the image sharpness,a single image rain removal method based on depth learning and image enhancement technology combined with residual and channel attention mechanism is proposed.Firstly, the rainy image is decomposed into the smooth base layer and the high-frequency detail layer by using the guided filter. Secondly,an adaptive gamma correction algorithm is proposed to enhance the smooth base layer to improve contrast. Thirdly, the deep neural network with residual block and the channel attention mechanism is constructed to remove rain in the high-frequency detail layer. Finally, the high-frequency detail layer after rain removal is combined with the enhanced smooth base layer to realize the single image rain removal. The experimental results show that compared with the representative single image rain removal method, the proposed method works well and can retain more image detail information.
|
|
Received: 11 October 2019
Published: 19 January 2021
|
|
|
|
Corresponding Authors:
Shi-hui ZHANG
E-mail: sshhzz@ysu.edu.cn
|
|
|
|
［1］Zhi T C, Pires B R, Hebert M, et al. Multispectral Imaging for Fine-Grained Recognition of Powders on Complex Backgrounds ［C］// Proceedings of 2019 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 8691-8700.
［2］Chen Y, Chen X D, Zhu J J, et al. Development of an autonomous unmanned surface vehicle with object detection using deep learning ［C］// Proceedings of 2018 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, USA, 2018: 5636-5641.
［3］郭茂森, 王宇, 张建国, 等. 最小控制递归平均算法对光纤声音传感系统的降噪作用 ［J］. 计量学报, 2019, 40 (5): 880-886.
Guo M, Wang Y, Zhang J, et al.Noise Reduction Effect Of Minimum Control Recursive Average Algorithm On Optical Fiber Acoustic Sensor System ［J］. Acta Metrologica Sinica, 2019, 40 (5): 880-886.
［4］温丽梅, 周苗苗, 李明, 等. 改进的Tikhonov正则化图像重建算法 ［J］. 计量学报, 2018, 39 (5): 679-683.
Wen L, Zhou M, Li M, et al. Improved Tikhonov Regularization Method for Image Reconstruction ［J］. Acta Metrologica Sinica, 2018, 39 (5): 679-683.
［5］Garg K, Nayar S K. Detection and removal of rain from videos ［C］// Proceedings of 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, USA, 2004, 1: 1528-1535.
［6］Zhang X P, Li H, Qi Y Y, et al. Rain Removal in Video by Combining Temporal and Chromatic Properties ［C］// Proceedings of 2006 IEEE International Conference on Multimedia and Expo, Toronto, Canada, 2006: 461-464.
［7］Kang L L, Lin C W, Fu Y H. Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition ［J］. IEEE Transactions on Image Processing, 2011, 21 (4): 1742-1755.
［8］Huang D A, Kang L W, Wang Y F. Self-Learning Based Image Decomposition With Applications to Single Image Denoising ［J］. IEEE Transactions on Multimedia, 2014, 16 (1): 83-93.
［9］Luo Y, Xu Y, Ji H. Removing Rain From a Single Image via Discriminative Sparse Coding ［C］// Proceedings of 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 3397-3405.
［10］Fu X Y, Huang J B, Ding X H, et al. Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal ［J］. IEEE Transactions on Image Processing, 2017, 26 (6): 2944-2956.
［11］Fu X Y, Huang J B, Zeng D L, et al. Removing Rain from Single Images via a Deep Detail Network ［C］// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1715-1723.
［12］Yang W, Tan R T, Feng J, et al. Deep Joint Rain Detection and Removal from a Single Image ［C］// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 1685-1694.
［13］Zhang H, Patel V M. Density-aware Single Image De-raining using a Multi-stream Dense Network ［C］// Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 695-704.
［14］Fan Z W, Wu H F, Fu X Y, et al. Residual-Guide Network for Single Image Deraining ［C］// Proceedings of 2018 ACM Multimedia Conference, Seoul, Korea, 2018: 1751-1759.
［15］He K M, Zhang X Y, Ren S Q, et al. Deep Residual Learning for Image Recognition ［C］// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
［16］Ledig C, Theis L, Huszar F, et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network ［C］// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 105-114.
［17］Hu J, Shen L, Sun G. Squeeze-and-Excitation Networks ［C］// Proceedings of 2018 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7132-7141.
［18］Wang Z, Bovik A C, Sheikh H R, et al. Image Quality Assessment: From Error Visibility to Structural Similarity［J］. IEEE Transactions on Image Processing, 2004, 13 (4): 600-612. |
|
|
|
2021, Vol. 42 
