Single Image Rain Removal Based on Deep Learning and Symmetry Transform
Abstract
:1. Introduction
2. Materials and Algorithms
2.1. Image Denoising Algorithm Based on Wavelet Transform
2.1.1. Characteristics of Wavelet Transform
2.1.2. Denoising Process
2.2. Raindrop Removal Model Based on Deep Learning
2.2.1. YUV Spatial Tansformation
2.2.2. Deep Feature Learning
2.2.3. Network Training Algorithm
3. Results
3.1. Denoising Experiment of Wavelet Transform
3.2. Simulated Rain Image
3.3. Experiment on Real Rain Image
4. Discussion
- (1)
- By analyzing the wavelet transform denoising experiments, we can know that the method in this paper has a better removal effect on mixed noise, which proves that this denoising algorithm has the advantages of maintaining good details and strong adaptability.
- (2)
- It can be known from the analysis and simulation of rainy image experiments that the method in this paper can effectively remove rain lines in the image, while retaining the details of the original image and avoiding the loss of feature information. This is because the algorithm in this paper is based on the traditional deep convolutional neural network and incorporates wavelet transform. When there are textures in the image that are close to the direction of the rain line, these algorithms will incorrectly process non-rain objects, resulting in blurred image details. Compared with other algorithms, the algorithm in this paper has better visual effects, and has greater advantages in removing rain lines and retaining original image information.
- (3)
- The analysis of real rain image experiments shows that the proposed convolutional neural network model is trained using synthetic rain map data. The proposed algorithm still has a good rain removal effect on images under real rain. Even when the rain bars are obvious, the algorithm proposed in this paper still effectively removes the rain bars on the real rain map.
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Images | 300 Virtual Images | Airplane | Bridge | |
---|---|---|---|---|
Peak signal-to-noise ratio | ID | 30.98 | 29.75 | 27.71 |
DSC | 32.66 | 36.72 | 35.07 | |
LP | 36.06 | 34.88 | 36.91 | |
Algorithm of this paper | 37.15 | 39.25 | 39.83 |
Images | 300 Virtual Images | Airplane | Bridge | |
---|---|---|---|---|
Structural similarity | ID | 0.88 | 0.81 | 0.89 |
DSC | 0.95 | 0.98 | 0.98 | |
LP | 0.95 | 0.95 | 0.98 | |
Algorithm of this paper | 0.97 | 0.99 | 0.99 |
Blind image quality index | Images | Street View |
Simulate rain images | 36.59 | |
ID | 42.45 | |
DSC | 34.68 | |
LP | 29.32 | |
algorithm of this paper | 27.81 |
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Share and Cite
Yang, Q.; Yu, M.; Xu, Y.; Cen, S. Single Image Rain Removal Based on Deep Learning and Symmetry Transform. Symmetry 2020, 12, 224. https://doi.org/10.3390/sym12020224
Yang Q, Yu M, Xu Y, Cen S. Single Image Rain Removal Based on Deep Learning and Symmetry Transform. Symmetry. 2020; 12(2):224. https://doi.org/10.3390/sym12020224
Chicago/Turabian StyleYang, Qing, Ming Yu, Yan Xu, and Shixin Cen. 2020. "Single Image Rain Removal Based on Deep Learning and Symmetry Transform" Symmetry 12, no. 2: 224. https://doi.org/10.3390/sym12020224
APA StyleYang, Q., Yu, M., Xu, Y., & Cen, S. (2020). Single Image Rain Removal Based on Deep Learning and Symmetry Transform. Symmetry, 12(2), 224. https://doi.org/10.3390/sym12020224