scholarly journals Design of median-type filters with an impulse noise detector using decision tree and particle swarm optimization for image restoration

2010 ◽  
Vol 7 (4) ◽  
pp. 859-882 ◽  
Author(s):  
Bae-Muu Chang ◽  
Hung-Hsu Tsai ◽  
Xuan-Ping Lin ◽  
Pao-Ta Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Decision Tree ◽  
High Performance ◽  
Impulse Noise ◽  
Median Filter ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Ordered Median ◽  
Mean Filter ◽  
Hybrid Detector

This paper proposes the median-type filters with an impulse noise detector using the decision tree and the particle swarm optimization, for the recovery of the corrupted gray-level images by impulse noises. It first utilizes an impulse noise detector to determine whether a pixel is corrupted or not. If yes, the filtering component in this method is triggered to filter it. Otherwise, the pixel is kept unchanged. In this work, the impulse noise detector is an adaptive hybrid detector which is constructed by integrating 10 impulse noise detectors based on the decision tree and the particle swarm optimization. Subsequently, the restoring process in this method respectively utilizes the median filter, the rank ordered mean filter, and the progressive noise-free ordered median filter to restore the corrupted pixel. Experimental results demonstrate that this method achieves high performance for detecting and restoring impulse noises, and outperforms the existing well-known methods.

scholarly journals DAMAGE DETECTION FOR UHPFRC COMMUNICATION TOWER BASED ON FREQUENCY DATA AND PARTICLE SWARM OPTIMIZATION

2019 ◽  
Vol 25 (6) ◽  
pp. 495-517
Author(s):  
Sarah Jabbar ◽  
Farzad Hejazi ◽  
Ammar N. Hanoon ◽  
Rizal S. M. Rashid
Keyword(s):  
Particle Swarm Optimization ◽  
High Performance ◽  
Particle Swarm ◽  
Correlation Factor ◽  
Fiber Reinforced Concrete ◽  
Swarm Optimization ◽  
Computational Procedures ◽  
The Stability ◽  
Reliability Performance ◽  
Communication Towers

Advances in the telecommunication and broadcasting sectors have increased the need for networking equipment of communication towers. Slender structures, such as towers, are sensitive to dynamic loads, such as vibration forces. Therefore, the stability and reliability performance of towers can be maintained effectively through the prompt detection, localization, and quantification of structural damages by obtaining the dynamic frequency response of towers. However, frequency analysis for damaged structures requires long computational procedures and is difficult to perform because of the damages in real structures, particularly in towers. Therefore, this study proposed a correlation factor that can identify the relationship between frequenciesunderhealthy and damaged conditions of ultra high performance fiber-reinforced concrete (UHPFRC) communication towers using particle swarm optimization. The finite element method was implemented to simulate three UHPFRC communication towers, and an experimental test was conducted to validate and verify the developed correlation factor

scholarly journals Effective Approach to Classify and Segment Retinal Hemorrhage Using ANFIS and Particle Swarm Optimization

2018 ◽  
Vol 27 (4) ◽  
pp. 681-697
Author(s):  
Lawrence Livingston Godlin Atlas ◽  
Kumar Parasuraman
Keyword(s):  
Particle Swarm Optimization ◽  
Median Filter ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Retinal Hemorrhage ◽  
Scale Invariant ◽  
Swarm Optimization ◽  
Segmented Images ◽  
Occurrence Matrix ◽  
Scale Invariant Feature

Abstract The main objective of this study is to progress the structure and segment the images from hemorrhage recognition in retinal fundus images in ostensible. The abnormal bleeding of blood vessels in the retina which is the membrane in the back of the eye is called retinal hemorrhage. The image folders are deliberated, and the filter technique is utilized to decrease the images specifically adaptive median filter in our suggested proposal. Gray level co-occurrence matrix (GLCM), grey level run length matrix (GLRLM) and Scale invariant feature transform (SIFT) feature skills are present after filtrating the feature withdrawal. After this, the organization technique is performed, specifically artificial neural network with fuzzy interface system (ANFIS) method; with the help of this organization, exaggerated and non-affected images are categorized. Affected hemorrhage images are transpired for segmentation procedure, and in this exertion, threshold optimization is measured with numerous optimization methods; on the basis of this, particle swarm optimization is accomplished in improved manner. Consequently, the segmented images are projected, and the sensitivity is great when associating with accurateness and specificity in the MATLAB platform.

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