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Pal, Subharun
- Multiframe Image Restoration Using Generative Adversarial Networks
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Authors
Affiliations
1 Department of Computer science and Technology, Karpagam College of Engineering, IN
2 Department of Computer Science and Engineering, Manipur Institute of Technology, Manipur University Campus, IN
3 Department of Computer Engineering, Dwarkadas Jivanlal Sanghvi College of Engineering, IN
4 Department of Computer Science and Engineering, Indian Institute of Technology Jammu, IN
1 Department of Computer science and Technology, Karpagam College of Engineering, IN
2 Department of Computer Science and Engineering, Manipur Institute of Technology, Manipur University Campus, IN
3 Department of Computer Engineering, Dwarkadas Jivanlal Sanghvi College of Engineering, IN
4 Department of Computer Science and Engineering, Indian Institute of Technology Jammu, IN
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3043-3048Abstract
This paper introduces a novel approach for multiframe image restoration using Generative Adversarial Networks (GANs). Traditional image restoration techniques often struggle with handling complex degradation patterns and noise in images. In contrast, GANs have demonstrated remarkable capability in generating realistic and high-quality images. The proposed method leverages the power of GANs to restore multiframe degraded images by training the generator to learn the underlying clean image from a set of degraded frames. The discriminator collaborates with the generator to ensure the fidelity of the restored output. Experimental results on various datasets show that the proposed multiframe image restoration approach achieves superior performance compared to state-of-the-art methods in terms of image quality and fidelity.Keywords
Multiframe, Image Restoration, Generative Adversarial Networks (GANs), Degradation Patterns, Fidelity.
References
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- Machine Learning-Based Facial Recognition for Video Surveillance Systems
Abstract Views :46 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, IN
2 Department of Electronics and Communication Engineering, R.M.K. Engineering College, IN
3 Department of Computer Science and Engineering, B.N. College of Engineering and Technology, IN
4 Department of Computer Science, National College, IN
5 SSM Research Center, Swiss School of Management, CH
1 Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, IN
2 Department of Electronics and Communication Engineering, R.M.K. Engineering College, IN
3 Department of Computer Science and Engineering, B.N. College of Engineering and Technology, IN
4 Department of Computer Science, National College, IN
5 SSM Research Center, Swiss School of Management, CH
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 2 (2023), Pagination: 3149-3154Abstract
Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model's performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.Keywords
Facial Recognition, Machine Learning, Video Surveillance, Deep Learning, Computer VisionReferences
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