scholarly journals Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization

2021 ◽  
Author(s):  
V Hyma Lakshmi Tirumani ◽  
Madhu Tenneti ◽  
Ch. Srikavya K ◽  
Sarat Kumar Kotamraju
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Wavelet Transforms ◽  
Particle Swarm ◽  
Image Resolution ◽  
Swarm Optimization

scholarly journals Sonar Objective Detection Based on Dilated Separable Densely Connected CNNs and Quantum-Behaved PSO Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Zhen Wang ◽  
Buhong Wang ◽  
Jianxin Guo ◽  
Shanwen Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Linear Interpolation ◽  
Image Resolution ◽  
Detection Accuracy ◽  
Swarm Optimization ◽  
Feature Information ◽  
Objective Detection ◽  
Detection Effect

Underwater sonar objective detection plays an important role in the field of ocean exploration. In order to solve the problem of sonar objective detection under the complex environment, a sonar objective detection method is proposed based on dilated separable densely connected convolutional neural networks (DS-CNNs) and quantum-behaved particle swarm optimization (QPSO) algorithm. Firstly, the dilated separable convolution kernel is proposed to extend the local receptive field and enhance the feature extraction ability of the convolution layers. Secondly, based on the linear interpolation algorithm, a multisampling pooling (MS-pooling) operation is proposed to reduce the feature information loss and restore image resolution. At last, with contraction-expansion factor and difference variance in the traditional particle swarm optimization algorithm introduced, the QPSO algorithm is employed to optimize the weight parameters of the network model. The proposed method is validated on the sonar image dataset and is compared with other existing methods. Using DS-CNNs to detect different kinds of sonar objectives, the experiments shows that the detection accuracy of DS-CNNs reaches 96.98% and DS-CNNs have better detection effect and stronger robustness.

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

2013 ◽  
Vol 760-762 ◽  
pp. 1389-1393
Author(s):  
Ren Tao Zhao ◽  
You Yu Wang ◽  
Hua De Li ◽  
Jun Tie
Keyword(s):  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Infrared Image ◽  
Optimal Solution ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Modified Pso

Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method

2021 ◽  
Vol 11 (12) ◽  
pp. 2897-2906
Author(s):  
K. Sangeetha ◽  
S. Prakash
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Particle Swarm Optimization ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Cancer Detection ◽  
Detection System ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.

scholarly journals Implementation of Modified Particle Swarm Optimization Algorithm for Image Contrast Enhancement

2020 ◽  
Vol 9 (4) ◽  
pp. 1030-1036
Keyword(s):  
Image Processing ◽  
Particle Swarm Optimization ◽  
Contrast Enhancement ◽  
Particle Swarm ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Image Contrast Enhancement ◽  
Overall Evaluation

Image contrast enhancement is mostly preferred approach for Medical Image Processing field. In this work, a new Histogram Specification (HS) based image contrast enhancement scheme is described. The optimal values of the weighing constraints are progressed through the standard of Modified Particle Swarm Optimization (MPSO) with respect to the histogram of the input processed images. The proposed scheme improves the contrast of the input processed image superior than its existing histogram equalization methods. Henceforth, this method can efficiently be utilized in the fields including image processing, electronics etc. The overall evaluation of the HS scheme can be computed by means of Discrete Entropy (DE) and Contrast Improvement Index (CII).

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