Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement
Abstract
:1. Introduction
 A new inertia weight function of PSO algorithm is constructed to make the weight coefficient change with the number of iterations and the current position of particles. Global search ability is increased in the early stage of search, and local search ability is strengthened in the late stage of search, so as to achieve the balance between local search and global search.
 The mechanism of jumping out of the local optimal solution is introduced into the PSO to avoid the algorithm falling into a local optimal solution.
 A new infrared image enhancement technology is proposed, which combines the advantages of bihistogram algorithm and dualdomain image decomposition to increase the contrast of the enhanced image without losing the image details.
2. Particle Swarm Optimization
2.1. Exponential Center Symmetry Inertia Weight Function
2.2. Local Optimal Solution Jumping Strategy
2.3. EXPSO Algorithm Flow
Algorithm 1 Pseudo code of EXPSO. 

3. Image Enhancement Method
3.1. Contrast Enhancement Based on BiHistogram Equalization
3.2. Detail Enhancement Based on DualDomain Image Decomposition
3.3. Fitness Function
4. Experiment
4.1. EXPSO Algorithm Performance Experiment
4.2. Infrared Image Enhancement Experiment
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
PSO  Particle Swarm Optimization 
EXPSO  Proposed improved (EXponential) PSO algorithm is this paper 
QPSO  Quantum Particle Swarm Optimization 
HFPSO  A Hybrid Firefly and Particle Swarm Optimization algorithm 
GQPSO  Gaussian Quantum behaved PSO algorithm 
HCQPSO  Hybrid Chaotic Quantum behaved PSO algorithm 
SRRM  Structure revealing Robust Retinex model 
BBHE  Brightness preserving BiHistogram equalization 
CLAHE  Contrast Limited Adaptive Histogram Equalization 
DPE  Deep Photo Enhancer 
EFF  Exposure Fusion Framework 
CRM  Camera Response Model 
JED  Joint Enhancement and Denoising Method via Sequential Decomposition 
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Author  Algorithm  Abbreviation  Year  Inspiration 

Askarzadeh et al. [6]  Bird Mating Optimizer  BMO  2012  Bird mating 
Gandomi et al. [7]  Krill Herd  KH  2012  Krill herd 
Pan et al. [8]  Fruit fly Optimization Algorithm  FOA  2012  Fruit fly 
Kaveh et al. [9]  Dolphin Echolocation  DE  2013  Dolphin 
Mirjalili et al. [10]  Grey Wolf Optimizer  GWO  2014  Grey wcolf 
Rosenberg [11]  Artificial Swarm Intelligence  ASI  2014  Human 
Mirjalili [12]  Ant Lion Optimization algorithm  ALO  2015  Ant lion 
Mirjalili et al. [13]  Whale Optimization Algorithm  WOA  2016  Whale 
Askarzadeh [14]  Crow Search Algorithm  GSA  2016  Crow 
Mirjalili [15]  Dragonfly Algorithm  DA  2016  Dragonfly 
Biyanto et al. [16]  Killer Whale Optimization  KWO  2017  Whale 
Mirjalili et al. [17]  Salp Swarm Algorithm  SSA  2017  Salp 
Alatas [18]  Sports Inspired Optimization  SIO  2019  Sports 
Name  Function  Range  ${f}_{\mathrm{min}}$ 

Ackley  $\begin{array}{c}f\left(x\right)=20+e20\mathrm{exp}\left(0.2\sqrt{\frac{1}{n}{\displaystyle \sum _{i=1}^{n}}{x}_{i}^{2}}\right)\hfill \\ \mathrm{exp}\left(\sqrt{\frac{1}{n}{\displaystyle \sum _{i=1}^{n}}\mathrm{cos}\left(2\pi {x}_{i}\right)}\right)\hfill \end{array}$  $\left[32,32\right]$  0 
Rastrigin  $f\left(x\right)={\displaystyle \sum _{i=1}^{n}}{\left({x}_{i}^{2}10\mathrm{cos}\left(2\pi {x}_{i}\right)+10\right)}^{2}$  $\left[5.12,5.12\right]$  0 
DeJongF4  $f\left(x\right)={\displaystyle \sum _{i=1}^{n}}i{x}_{i}^{4}$  $\left[100,100\right]$  0 
alpin  $f\left(x\right)={\displaystyle \sum _{i=1}^{n}}\left{x}_{i}\mathrm{sin}{x}_{i}+0.1i\right$  $\left[50,50\right]$  0 
Rosenbrock  $f\left(x\right)={\displaystyle \sum _{i=1}^{n1}}\left(100{\left({x}_{i+1}{x}_{i}^{2}\right)}^{2}+{\left(1{x}_{i}\right)}^{2}\right)$  $\left[5,5\right]$  0 
Sphere  $f\left(x\right)={\displaystyle \sum _{i=1}^{n}}{x}_{i}^{2}$  $\left[100,100\right]$  0 
Algorithms  Img1  Img2  Img3  Img4  Img5  Mean 

Input  6.32  6.62  6.96  6.40  6.86  6.63 
SRRM  6.24  6.05  7.05  5.87  6.29  6.30 
BBHE  7.13  7.41  7.10  7.19  7.17  7.20 
CLAHE  5.55  5.85  5.90  5.55  5.94  5.76 
DPH  6.96  7.47  7.54  6.93  7.08  7.20 
EFF  6.38  6.48  7.05  6.26  6.69  6.57 
CRM  6.24  6.30  7.00  6.07  6.37  6.40 
JED  6.25  6.05  7.02  5.88  6.30  6.30 
Ours  7.20  7.55  7.57  7.26  7.18  7.35 
Algorithms  Img1  Img2  Img3  Img4  Img5  Mean 

Input  5.95  4.11  5.47  5.26  4.01  4.96 
SRRM  6.62  13.16  7.36  4.59  3.10  6.97 
BBHE  12.80  9.80  11.00  12.35  9.89  11.17 
CLAHE  13.50  12.58  12.20  16.34  10.13  12.95 
DPH  17.89  10.85  10.41  16.77  10.22  13.23 
EFF  7.34  4.39  6.28  5.69  4.31  5.60 
CRM  8.57  4.73  7.35  6.19  4.69  6.31 
JED  6.06  2.87  5.70  4.21  2.81  4.33 
Ours  16.60  13.25  15.33  17.09  12.64  14.98 
Algorithms  Img1  Img2  Img3  Img4  Img5  Mean 

Input  33.22  16.42  46.64  24.69  13.22  26.84 
SRRM  56.05  13.56  104.74  29.35  10.32  42.80 
BBHE  139.70  79.62  141.98  120.85  71.09  110.65 
CLAHE  133.38  131.20  205.04  181.97  87.57  147.83 
DPH  174.06  74.06  70.73  103.37  66.60  97.76 
EFF  46.83  17.76  59.68  29.08  14.93  33.66 
CRM  61.99  19.71  78.51  35.33  17.48  42.60 
JED  49.12  11.79  87.60  26.95  9.07  36.91 
Ours  221.21  131.52  271.25  216.29  111.99  190.45 
Algorithms  Img1  Img2  Img3  Img4  Img5  Mean 

Input  3.94  6.35  4.19  7.70  6.33  5.70 
SRRM  4.14  6.06  3.74  6.57  6.02  5.31 
BBHE  3.87  6.02  3.02  6.34  5.79  5.01 
CLAHE  3.79  6.78  2.44  7.15  5.96  5.22 
DPH  4.05  6.06  3.79  6.51  6.31  5.34 
EFF  3.97  5.42  3.05  5.75  5.78  4.79 
CRM  3.95  5.57  2.88  5.73  5.45  4.72 
JED  3.84  5.64  4.00  4.74  5.78  4.80 
Ours  3.78  5.53  3.44  4.34  5.71  4.56 
Algorithms  Img1  Img2  Img3  Img4  Img5  Mean 

Input  19.74  28.35  27.81  30.10  17.40  24.68 
SRRM  20.47  23.20  22.13  26.96  19.30  22.41 
BBHE  29.69  30.64  14.06  34.00  19.39  25.56 
CLAHE  29.52  31.07  12.53  36.12  16.41  25.13 
DPH  30.63  23.34  21.46  23.53  23.06  24.40 
EFF  15.96  28.35  24.32  31.14  17.60  23.47 
CRM  21.73  28.42  19.11  32.06  18.62  23.99 
JED  20.21  23.23  29.09  30.02  29.86  26.48 
Ours  26.83  23.18  22.14  21.30  16.21  21.93 
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Qin, C.; Gu, X. Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement. Symmetry 2020, 12, 248. https://doi.org/10.3390/sym12020248
Qin C, Gu X. Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement. Symmetry. 2020; 12(2):248. https://doi.org/10.3390/sym12020248
Chicago/Turabian StyleQin, Chaoxuan, and Xiaohui Gu. 2020. "Improved PSO Algorithm Based on Exponential Center Symmetric Inertia Weight Function and Its Application in Infrared Image Enhancement" Symmetry 12, no. 2: 248. https://doi.org/10.3390/sym12020248