Image Noise Removal via Hierarchical Subbands Shrinkage Modified by Particle Swarm Optimization

2007 ◽  
Author(s):  
Baolong Guo ◽  
Yunyi Yan ◽  
Xiang Fu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Noise Removal ◽  
Image Noise ◽  
Swarm Optimization

Cellular Neural Network Based on Hybrid Linear Matrix Inequality and Particle Swarm Optimization for Noise Removal of Color Image

2011 ◽  
Vol 422 ◽  
pp. 771-774
Author(s):  
Te Jen Su ◽  
Jui Chuan Cheng ◽  
Yu Jen Lin
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Linear Matrix Inequality ◽  
Color Image ◽  
Particle Swarm ◽  
Cellular Neural Network ◽  
Noise Removal ◽  
Linear Matrix ◽  
Matrix Inequality ◽  
Swarm Optimization

This paper presents a color image noise removal technique that employs a cellular neural network (CNN) based on hybrid linear matrix inequality (LMI) and particle swarm optimization (PSO). For designing templates of CNN, the Lyapunov stability theorem is applied to derive the criterion for the uniqueness and global asymptotic stability of the CNN’s equilibrium point. The template design is characterized as a standard LMI problem, and the parameters of templates are optimized by PSO. The input templates are obtained by employing the CNN’s property of saturation nonlinearity, which can be used to eliminate noise from arbitrary corrupted images. The demonstrated examples are compared favorably with other available methods, which illustrate the better performance of the proposed LMI-PSO-CNN methodology.

scholarly journals Assessment of Joint Space in Knee Osteoarthritis using Particle Swarm Optimization Technique

2019 ◽  
Vol 8 (12) ◽  
pp. 5818-5823
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Automated Analysis ◽  
Joint Space ◽  
Noise Removal ◽  
Swarm Optimization ◽  
Overweight People ◽  
Joint Disorder ◽  
Particle Swarm Optimization Technique

Osteoarthritis (OA) is one of the most common joint disorder which is debility seen in elderly & overweight people which affects the cartilage of bone joints like knee, feet, hip, and spine. In OA usually, cartilage is ruptured due to the kneading of bones with each other which will end up causing severe pain. In this condition, it is necessary to analyze the severity of OA which involves various medical imaging and clinical examination techniques. In this paper, automated analysis and detection of OA are proposed by calculating the thickness of cartilage which also helps to effectively detect and analyze the abnormalities in bone structures. Where we have considered various knee X-ray images. Initially, preprocessing and noise removal is performed. Further by implementing Particle Swarm Optimization (PSO) segmentation and thresholding, the specified knee region is cropped and analyzed to calculate the thickness of cartilage to detect the presence of OA.

An Intelligent Particle Swarm Optimization with Convolutional Neural Network for Diabetic Retinopathy Classification Model

2021 ◽  
Vol 11 (3) ◽  
pp. 803-809
Author(s):  
J. Jayanthi ◽  
T. Jayasankar ◽  
N. Krishnaraj ◽  
N. B. Prakash ◽  
A. Sagai Francis Britto ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Particle Swarm Optimization ◽  
Convolutional Neural Network ◽  
Vision Loss ◽  
Particle Swarm ◽  
Noise Removal ◽  
Classification Model ◽  
Swarm Optimization

Diabetic retinopathy (DR), a major cause of vision loss and it raises a major issue among diabetes people. DR considerably affect the financial condition of the society specially in medicinal sector. Once proper treatment is given to the DR patients, roughly 90% of patients can be saved from vision loss. So, it is needed to develop a DR classification model for classifying the stages and severity level of DR to offer better treatment. This article develops a novel Particle Swarm Optimization (PSO) algorithm based Convolutional Neural Network (CNN) Model called PSO-CNN model to detect and classify DR from the color fundus images. The proposed PSO-CNN model comprises three stages namely preprocessing, feature extraction and classification. Initially, preprocessing is carried out as a noise removal process to discard the noise present in the input image. Then, feature extraction process using PSO-CNN model is applied to extract the useful subset of features. Finally, the filtered features are given as input to the decision tree (DT) model for classifying the set of DR images. The simulation of the PSO-CNN model takes place using a benchmark DR database and the experimental outcome stated that the PSO-CNN model has outperformed all the compared methods in a significant way. The outcome of the simulation process indicated that the PSO-CNN model has offered maximum results.

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