FEGAO: A Revolutionary Method for Enhancing Defective Fuzzy Images with Non-Linear Refinement
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 4
Abstract
This study presents a novel image restoration method, designed to enhance defective fuzzy images, by utilizing the Fuzzy Einstein Geometric Aggregation Operator (FEGAO). The method addresses the challenges posed by non-linearity, uncertainty, and complex degradation in defective images. Traditional image enhancement approaches often struggle with the imprecision inherent in defect detection. In contrast, FEGAO employs the Einstein t-norm and t-conorm for non-linear aggregation, which refines pixel coordinates and improves the accuracy of feature extraction. The proposed approach integrates several techniques, including pixel coordinate extraction, regional intensity refinement, multi-scale Gaussian correction, and a layered enhancement framework, thereby ensuring superior preservation of details and minimization of artifacts. Experimental evaluations demonstrate that FEGAO outperforms conventional methods in terms of image resolution, edge clarity, and noise robustness, while maintaining computational efficiency. Comparative analysis further underscores the method’s ability to preserve fine details and reduce uncertainty in defective images. This work offers significant advancements in image restoration by providing an adaptive, efficient solution for defect detection, machine vision, and multimedia applications, establishing a foundation for future research in fuzzy logic-based image processing under degraded conditions.
Authors and Affiliations
Ibrar Hussain
Keywords
Related Articles
- EP ID EP761608
- DOI https://doi.org/10.56578/ida030405
- Views 23
- Downloads 0
Critical Factors Influencing Cloud Security Posture of Enterprises: An Empirical Analysis
This study examines the aspects that can impact an organization's cloud security posture and the consequences for their cloud adoption strategies. Based on a thorough examination of existing literature, a conceptual fram...
A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans
Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' qu...
Comparative Analysis of Machine Learning Algorithms for Daily Cryptocurrency Price Prediction
The decentralised nature of cryptocurrency, coupled with its potential for significant financial returns, has elevated its status as a sought-after investment opportunity on a global scale. Nonetheless, the inherent unpr...
Ensemble Learning Applications in Multiple Industries: A Review
This study proposes a systematic review of the application of Ensemble learning (EL) in multiple industries. This study aims to review prevailing application in multiple industries to guide for the future landing applica...
Optimizing Energy Storage and Hybrid Inverter Performance in Smart Grids Through Machine Learning
The effective integration of renewable energy sources (RES), such as solar and wind power, into smart grids is essential for advancing sustainable energy management. Hybrid inverters play a pivotal role in the conversio...