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Singh, Amanpreet
- Evaluation of Improved Fuzzy Inference System to Preserve Image Edge for Image Analysis
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, I.K. Gujral Punjab Technical University, IN
2 Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, IN
1 Department of Electronics and Communication Engineering, I.K. Gujral Punjab Technical University, IN
2 Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2423-2431Abstract
There are numerous applications based on edge detection have been used in the area of image analysis. The technique of edge detection is an important step towards the visual system reliability and security that delivers a better understanding in many applications like object recognition classification, photography, and many more others computer vision application such as pedestrian detection for a vehicle on the road, face detection in biometric, and video surveillance. We know that detection of edge detection is a scientific technique that is practiced to provide better image analysis and towards this purpose, lots of edge identification approach was already implemented by the researchers in the image processing era, but they do not achieve acceptable results for all types of the image that can help in the image analysis. In this research, we introduced a comparative evaluation of edge detection algorithms for instance Sobel, Canny, and Fuzzy logic-based edge detector with an Improved Fuzzy Inference (IFI) system is presented to preserve image edge for image analysis. The key contribution of this research is developing a new hybrid edge mechanism by utilizing the gradient and standard deviation based fuzzy logic approach to achieve better edge detection efficiency. To provide a better edge or non-edge region from an image the proposed IFI has its impact on quality parameters, for instance, Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Entropy and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of the proposed IFI system is compared with other edge technique and we observed that the achieved results justify the proposed work in image processing.Keywords
Edge Detection, Fuzzy Logic, Fuzzy Inference, Gradient, Standard Deviation.
References
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- A Comparative Framework For Blocking Artifacts Removal Of Digital Images Using Hybrid Mechanism
Abstract Views :190 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, IK Gujral Punjab Technical University, IN
2 Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, IN
1 Department of Electronics and Communication Engineering, IK Gujral Punjab Technical University, IN
2 Department of Electronics and Communication Engineering, DAV Institute of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2630-2637Abstract
The restoration of an image with blocking artifacts due to compression at low bit rates is a challenging task and blocking artifact measurement algorithms have an important role to play in the computer vision field. An artifacts removal technique is an important step towards the reliability and security of image processing area that delivers a better understanding in many applications like pattern recognition, object classification, surveillance system and many more. We know that the removal of art objects is a scientific method used to provide better image analysis and for this purpose many methods of removal of art objects were already made by researchers during the processing of images such as line, motion, pattern, and hair. But in availability of group of artifacts in an image, they do not achieve an acceptable result. In this research, we proposed a comparative framework for blocking artifacts removal of digital images using hybrid mechanism. The main contribution of this research is developing a new neuro-fuzzy systembased hybrid artifacts removal mechanism to achieve better blocking artifacts efficiency. To remove artifact from an image the proposed framework has its own impact in quality parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity (SSIM) with the execution time. At last, the performance parameters of proposed framework is compare for all five techniques such as line, motion, pattern, hair and combination of all with each other and we observed that the achieved results justify the proposed hybrid artifact removal method in the field of image processing.Keywords
Artifacts, Line, Motion, Pattern, Hair, Neuro-Fuzzy, Image processing, PSNR, MSE, SSIM, Execution TimeReferences
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