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Ponnusamy, Muruganantham
- Mathematical Morphology based Digital Image Enhancement Processing with Cross Separate Boundary Objects
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Authors
Affiliations
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2699-2703Abstract
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression.Keywords
Digital Image Processing, Computer Algorithms, Digital Images, Mathematical MorphologyReferences
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- Deep Reinforcement Learning-based Optimization and Enhancement of Multimedia Data : An Innovative Approach
Abstract Views :112 |
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Authors
Affiliations
1 Department of Mechanical Engineering, Indian Institute of Information Technology Kalyani, IN
2 Department of Computer Science and Engineering, Joginpally B. R. Engineering College, IN
3 Department of Electronics and Communication Engineering, CVR College of Engineering, IN
4 Business Analytics Department, University of Rochester - Simon Business School, US
1 Department of Mechanical Engineering, Indian Institute of Information Technology Kalyani, IN
2 Department of Computer Science and Engineering, Joginpally B. R. Engineering College, IN
3 Department of Electronics and Communication Engineering, CVR College of Engineering, IN
4 Business Analytics Department, University of Rochester - Simon Business School, US
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 3013-3020Abstract
The rapid growth of multimedia data in various domains has necessitated the development of efficient techniques to enhance and optimize its quality. Traditional approaches often struggle to address the complexity and diversity of multimedia data, leading to suboptimal results. This paper presents a novel approach to tackle this challenge by leveraging the power of deep reinforcement learning (DRL). The proposed method utilizes DRL to learn and optimize multimedia data in an improvised manner. By employing a combination of convolutional neural networks and deep Q-networks, the model can effectively extract high-level features and make informed decisions to enhance the quality of multimedia data. The reinforcement learning framework enables the system to learn from its actions, continuously improving its performance through an iterative process. To evaluate the effectiveness of the proposed method, extensive experiments were conducted using a diverse set of multimedia datasets. The results demonstrate significant improvements in various quality metrics, including image resolution, video frame rate, and audio clarity. Additionally, the proposed approach exhibits robustness across different types of multimedia data, ensuring consistent enhancement performance across various domains. Furthermore, the computational efficiency of the proposed method is also highlighted, as it demonstrates faster convergence and lower computational overhead compared to traditional optimization methods. This makes the approach practical for real-time applications where multimedia data needs to be processed efficiently. Overall, this paper introduces an innovative framework that combines deep reinforcement learning with multimedia data optimization. The results indicate its potential for enhancing multimedia data quality, offering a promising solution to the challenges associated with traditional approaches. The proposed method not only improves the visual and auditory aspects of multimedia content but also provides a scalable and efficient solution for real-world applications in domains such as image processing, video streaming, and audio analysis.Keywords
Deep Reinforcement Learning, Multimedia Data, Optimization, Enhancement, Convolutional Neural Networks, Deep Q-Networks, Quality Metrics, Computational Efficiency.References
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