Research on image de-raining method based on high scale rain pattern image block training algorithm
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19 mar 2025
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 19 mar 2025
Ricevuto: 02 nov 2024
Accettato: 09 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0355
Parole chiave
© 2025 Kan Ni et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Experimental training and test use of data sets
| Training set | Test set | |
|---|---|---|
| Heavy rain image data set | Rain Train H | Rain100H |
| Rain Train H | Rain300H | |
| Small rain image data set | Rain Train L | Rain100L |
| Rain Train L | Rain300L | |
| Irregular pattern data set | Rain Train H | Rain10 |
| Multi-type rain striped data set | Rain12600 | Rain1500 |
| Real rain chart data set | Rain12600 | SPA-data |
Experimental environment
| Device name | Equipment type | Detailed parameter information |
|---|---|---|
| Operating system | Windows 10 | E7-2620 |
| CPU | I7-8700K | 2.1GHz |
| GPU | NVIDIA RTX 3060 | 64G |
| Memory | DDR4 2666 hackers | 16G×2 |
| Hard disk | 860EVO | 1TB |
The ablation experiment is compared (SSIM/PSNR)
| Data set | SSIM/PSNR | Single use rain fog model module | Single use multiscale convolution module | NLSTM | Based on CNN’s high ratio rain stripe image to rain model |
|---|---|---|---|---|---|
| Rain100H | SSIM | 0.875 | 0.922 | 0.896 | 0.941 |
| PSNR | 30.44 | 29.55 | 21.65 | 30.79 | |
| Rain100L | SSIM | 0.961 | 0.971 | 0.874 | 0.981 |
| PSNR | 34.96 | 35.43 | 20.14 | 35.67 | |
| Rain300H | SSIM | 0.873 | 0.913 | 0.803 | 0.939 |
| PSNR | 28.89 | 29.68 | 22.69 | 30.65 | |
| Rain300L | SSIM | 0.968 | 0.975 | 0.878 | 0.987 |
| PSNR | 35.51 | 34.78 | 21.41 | 36.12 | |
| Rain10 | SSIM | 0.957 | 0.908 | 0.952 | 0.966 |
| PSNR | 36.42 | 36.69 | 24.57 | 36.58 |