Open Access
Subscription Access
Open Access
Subscription Access
Image Representation and Rendering From Low-Resolution Surveillance Videos Using Densenet
Subscribe/Renew Journal
In surveillance, the need for enhanced image representation and rendering from low-resolution videos is paramount for effective analysis and decision-making. This research addresses the limitations of conventional methods in extracting meaningful information from low-resolution footage. The prevalent challenge lies in the compromised clarity and detail inherent in surveillance videos, hindering accurate identification and analysis of critical events. The ubiquity of surveillance cameras has led to an influx of low-resolution videos, limiting the efficacy of traditional image processing techniques. This research aims to bridge this gap by leveraging DenseNet, a densely connected convolutional neural network (CNN) known for its ability to capture intricate features. The DenseNet seeks to enhance the representation and subsequent rendering of images, transcending the constraints imposed by low resolutions. The network ability to capture intricate details will be harnessed to enhance image representation. Subsequent rendering techniques will be employed to reconstruct high-quality images for improved analysis. The results showcase promising advancements in image representation and rendering using DenseNet. The enhanced visual quality of surveillance images allows for more precise identification and analysis of events, demonstrating the potential impact of the proposed methodology on improving surveillance systems.
Keywords
Surveillance, Low-Resolution Videos, DenseNet, Image Representation, Rendering
Subscription
Login to verify subscription
User
Font Size
Information
- X. Gao, J. Szep, S. Shao and S. Hariri, “Selecting Post-Processing Schemes for Accurate Detection of Small Objects in Low-Resolution Wide-Area Aerial Imagery”, Remote Sensing, Vol. 14, No. 2, pp. 255-262, 2022.
- Z. Chen and X. Xie, “CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution”, Proceedings of IEEE/CVF International Conference on Computer Vision, pp. 21185-21195, 2023.
- Z.T. Jahromi, S.M.T. Hasheminejad and S.V. Shojaedini, “Deep Learning Semantic Image Synthesis: A Novel Method for Unlimited Capacity, High Noise Resistance Coverless Video Steganography”, Multimedia Tools and Applications, Vol. 89, pp. 1-19, 2023.
- Z. Zhao and W. Gao, “Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation”, Electronics, Vol. 12, No. 13, pp. 2773-2779, 2023.
- Y. Zhang and C. Busch, “NTIRE 2023 Challenge on Image Super-Resolution (X4): Methods and Results”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1864-1883, 2023.
- C. Niu and D. Tarapore, “An Embarrassingly Simple Approach for Visual Navigation of Forest Environments”, Frontiers in Robotics and AI, Vol. 67, pp. 10-19, 2023.
- D.C. Lepcha and V. Goyal, “Image Super-Resolution: A Comprehensive Review, Recent Trends, Challenges and Applications”, Information Fusion, Vol. 91, pp. 230-260, 2023.
- K. Chauhan and R. Sharma, “Deep Learning-based Single-Image Super-resolution: A Comprehensive Review”, IEEE Access, Vol. 9, pp. 1-12, 2023.
- A. Greco, M. Vento and V. Vigilante, “Benchmarking Deep Networks for Facial Emotion Recognition in the Wild”, Multimedia Tools and Applications, Vol. 82, No. 8, pp. 11189-11220, 2023.
- T.A. Kadhim and D. Ben Aissa, “A Face Recognition Application for Alzheimer’s Patients using ESP32-CAM and Raspberry Pi”, Journal of Real-Time Image Processing, Vol. 20, No. 5, pp. 100-114, 2023.
Abstract Views: 48
PDF Views: 1