| Title | Image Denoising for Video Surveillance Cameras Based on Deep Learning Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Khasanova, Aliia, Makhmutova, Alisa, Anikin, Igor |
| Conference Name | 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) |
| Keywords | Cameras, Computer vision, Deep Learning, deep video, image denoising, Image quality, Measurement, Metrics, Neural networks, object detection, pubcrawl, Rain, resilience, Resiliency, Scalability, Snow, Traffic Control |
| Abstract | Nowadays, video surveillance cameras are widely used in many smart city applications for ensuring road safety. We can use video data from them to solve such tasks as traffic management, driving control, environmental monitoring, etc. Most of these applications are based on object recognition and tracking algorithms. However, the video image quality is not always meet the requirements of such algorithms due to the influence of different external factors. A variety of adverse weather conditions produce noise on the images, which often makes it difficult to detect objects correctly. Lately, deep learning methods show good results in image processing, including denoising tasks. This work is devoted to the study of using these methods for image quality enhancement in difficult weather conditions such as snow, rain, fog. Different deep learning techniques were evaluated in terms of their impact on the quality of object detection/recognition. Finally, the system for automatic image denoising was developed. |
| DOI | 10.1109/ICIEAM51226.2021.9446438 |
| Citation Key | khasanova_image_2021 |
Image Denoising for Video Surveillance Cameras Based on Deep Learning Techniques