Estimation and optimization based ill-posed inverse restoration using fuzzy logic

Intelligent systems ranging from neural network, evolutionary computations and swarm intelligence to fuzzy systems are extensively exploited by researchers to solve variety of problems. In this paper focus is on deblurring that is considered as an inverse problem. It becomes ill-posed when noise con...

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Veröffentlicht in:Multimedia tools and applications Jg. 69; H. 3; S. 1067 - 1087
Hauptverfasser: Bilal, Mohsin, Hussain, Ayyaz, Jaffar, Muhammad Arfan, Choi, Tae-Sun, Mirza, Anwar M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.04.2014
Springer
Springer Nature B.V
Schlagworte:
Algebra
Algorithms
Analysis
Applied sciences
Artificial intelligence
Computer Communication Networks
Computer Science
Computer science; control theory; systems
Connectionism. Neural networks
Data Structures and Information Theory
Digitization
Estimating techniques
Exact sciences and technology
Fuzzy logic
Ill posed problems
Inverse problems
Methods
Multimedia computer applications
Multimedia Information Systems
Neural networks
Noise
Optimization
Parameter estimation
Pattern recognition. Digital image processing. Computational geometry
Regularization methods
Remedies
Smoothness
Special Purpose and Application-Based Systems
Studies
Deblurring
Constrained least square error
Steepest descent
Quadratic functional regularization
Blur and noise
Fuzzy regularization parameter
Parameter estimation
Image processing
Noise reduction
Evolutionary algorithm
Steepest descent method
Expert system
Modeling
Optimization
Restoration
Efficiency
Least squares method
Swarm intelligence
Ill posed problem
Robustness
System identification
Intelligent system
Error function
Neural network
Blurred image
Inverse problem
Fuzzy logic
Artificial intelligence
Quadratic functional
Signal to noise ratio
ISSN:1380-7501, 1573-7721
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Beschreibung
Zusammenfassung:Intelligent systems ranging from neural network, evolutionary computations and swarm intelligence to fuzzy systems are extensively exploited by researchers to solve variety of problems. In this paper focus is on deblurring that is considered as an inverse problem. It becomes ill-posed when noise contaminates the blurry image. Hence the problem is very sensitive to small perturbation in data. Conventionally, smoothness constraints are considered as a remedy to cater the sensitivity of the problem. In this paper, fuzzy rule based regularization parameter estimation is proposed with quadratic functional smoothness constraint. For deblurring image in the presence of noise, a constrained least square error function is minimized by the steepest descent algorithm. Visual results and quantitative measurements show the efficiency and robustness of the proposed technique compared to the state of the art and recently proposed methods.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-012-1172-3