A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image
Abstract
:1. Introduction
1.1. Research Background
1.2. Related Works
1.3. Contributions
- (1)
- The population is initialized by fractional chaotic mapping to make the initial individual sparrows distributed as evenly as possible.
- (2)
- The locations of the finder and scroungers are updated with the Pareto distribution to speed up its global convergence and to avoid falling into a local optimum.
- (3)
- The standard deviation, edge content, and entropy are integrated into the evaluation function to assess the enhancement effect of the obtained iris image.
2. Methods
2.1. The Sparrow Search Algorithm
2.2. Chaotic Pareto Sparrow Search Algorithm
2.2.1. Initializing Population with Fractional Chaotic Sequence
- (1)
- Let , , , and .
- (2)
- Two chaotic sequences or are generated by Equation (8).
- (3)
- Take one of the chaotic sequences in step 2, for instance, , and map it to the solution space of the problem to be solved.
2.2.2. Updating Finders’ and Scroungers’ Locations
2.3. Overview of CLAHE
2.4. CPSSA-CLAHE
2.4.1. The Pseudocode
Algorithm 1 CPSSA-CLAHE |
Input: Biometric images, number of subimages, range of clip limit, the alarm value , the maximum iterations , the number of sparrows n, the number of finders , number of threatened sparrows |
Output: A CPSSA-CLAHE enhanced biometric image |
1: Initializing population with fractional chaotic sequence |
2: while do |
3: According to the location of each sparrow, that is, the clip limit, enhance the image using CLAHE |
4: Compute Entropy(H), Edge Content(S), and Standard Deviation (Std) of the enhanced image |
5: Get the fitness values of all sparrows. |
6: Sort the fitness values. |
7: Get the current best location (Gbest) and the current worst location (Gworst). |
8: Update the location of sparrow by using Equations (3), (11) and (12) |
9: Get the current new location; |
10: If the new location is better than before, update it; |
11: |
12: Get the best clip limit. |
13: Output the enhanced image using CLAHE |
2.4.2. The Fitness Function
3. Experiments and Discussion
3.1. Benchmark Function Comparison Experiment
3.2. Long Distance Iris Image Enhancement
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Authors | Algorithms | Strategy |
---|---|---|
Maurya et al. [16] | Cuckoo search (CS) algorithm | CSA is used to balance the contrast and brightness |
Nickfarjam et al. [17] | Modified PSO algorithm | Consists of the standard deviation and edge content |
Sathiyabhama, B et al. [18] | Gray wolf optimizer algorithm | Improve with rough set theory |
Qin et al. X [19] | Modified PSO algorithm | A modified inertia weight function used in the PSO |
Acharya et al. [20] | Modified genetic (GA) algorithm | Adaptive histogram equalization technique used in the GA |
Muniyappan et al. [21] | Adaptive genetic algorithm | Introduce adaptive crossover and mutation operations in GA |
Bhandari et al. [22] | CS algorithm | Improve the contrast of low-contrast image using CSA |
Kamoona et al. [23] | Modified CS algorithm | Image transform enhancement functions and objective function |
Prasath et al. [24] | Modified CS algorithm | Distance-Oriented Cuckoo Search (DOCS) algorithm |
Sridevi et al. [25] | Modified genetic algorithm | Fractional Genetic Algorithm |
Chen et al. [26] | Artificial bee colony algorithm | A new fitness function and new image transformation function |
Banharnsakun et al. [27] | Artificial bee colony algorithm | Image edge detection enhancement using ABC algorithm |
Type | Benchmark Functions | Dim | Range | |
---|---|---|---|---|
Type 1 | 30 | [−100, 100] | 0 | |
Type 1 | 30 | [−10, 10] | 0 | |
Type 1 | 30 | [−100, 100] | 0 | |
Type 1 | 30 | [−100, 100] | 0 | |
Type 2 | 30 | [−500, 500] | ||
Type 2 | 30 | [−50, 50] | 0 | |
Type 2 | 30 | [−50, 50] | 0 | |
Type 3 | 2 | [−65, 65] | 1 | |
Type 3 | 2 | [−5, 5] | −1.0316 | |
Type 3 | 4 | [0, 10] | −10.1532 | |
Type 3 | 4 | [0, 10] | −10.4028 | |
Type 3 | 4 | [0, 10] | −10.5363 |
F | SI | Best | Ave | Std | Computation Time (s) |
---|---|---|---|---|---|
F1 | PSO | 7.134288 | 1.959255 | 60.0022 | 1.157395 |
F1 | ABC | 4.574426 | 8.274577 | 2.3966 | 15.104270 |
F1 | SSA | 0 | 1.874825 | 1.02688 | 4.107282 |
F1 | CPSSA | 0 | 0 | 0 | 12.062264 |
F2 | PSO | 8.160150 | 3.057447 | 20.0995 | 1.23973 |
F2 | ABC | 2.483748 | 3.248522 | 23.3522 | 15.511821 |
F2 | SSA | 9.537863 | 7.955561 | 4.34102 | 4.154662 |
F2 | CPSSA | 0 | 0 | 0 | 11.906737 |
F3 | PSO | 2.908686 | 8.612808 | 5414.9 | 6.260316 |
F3 | ABC | 3.853256 | 6.251440 | 10,947.1 | 26.059873 |
F3 | SSA | 1.063104 | 4.102431 | 2.24699 | 11.141056 |
F3 | CPSSA | 0 | 0 | 0 | 17.316412 |
F4 | PSO | 5.852877 | 1.591048 | 75.627 | 1.182 |
F4 | ABC | 3.448549 | 1.015906 | 4.00236 | 15.4631 |
F4 | SSA | 2.680141 | 5.880191 | 2.10753 | 4.12826 |
F4 | CPSSA | 0 | 0 | 0 | 11.9581 |
F5 | PSO | −9.476918 | −7.601357 | 1069.6 | 1.9245 |
F5 | ABC | −1.453578 | −8.408056 | 2.94628 | 21.44868 |
F5 | SSA | −9.937985 | −8.648037 | 656.408 | 5.2218 |
F5 | CPSSA | −1.256949 | −1.252106 | 159.05 | 12.4857 |
F6 | PSO | 1.101431 | 4.680629 | 2.56227 | 8.91828 |
F6 | ABC | 1.108390 | 4.299645 | 423892 | 32.38175 |
F6 | SSA | 6.197603 | 2.995160 | 9.32387 | 15.1853 |
F6 | CPSSA | 1.570545 | 1.570545 | 5.5674 | 19.40687 |
F7 | PSO | 4.333554 | 15.26170 | 9.07786 | 9.031767 |
F7 | ABC | 1.039235 | 1.084383 | 1.07069 | 32.5297 |
F7 | SSA | 6.609665 | 1.112171 | 1.86574 | 15.10169 |
F7 | CPSSA | 1.349784 | 1.349784 | 5.5674 | 19.5371 |
F8 | PSO | 9.980038 | 9.980038 | 2.4774 | 14.990997 |
F8 | ABC | 9.980038 | 9.980069 | 5.79252 | 47.77464 |
F8 | SSA | 9.980038 | 4.184485 | 4.83682 | 23.997373 |
F8 | CPSSA | 9.980038 | 1.387087 | 2.1311 | 15.59964 |
F9 | PSO | −1.031628 | −1.031608 | 3.10204 | 1.094425 |
F9 | ABC | −1.031628 | −1.031628 | 3.47478 | 15.420527 |
F9 | SSA | −1.031628 | −1.031628 | 6.25324 | 3.8147 |
F9 | CPSSA | −1.031628 | −1.031628 | 6.14542 | 1.82324 |
F10 | PSO | −10.1532 | −9.399145 | 2.06047 | 1.598298 |
F10 | ABC | −10.1532 | −10.1532 | 1.20822 | 16.5355 |
F10 | SSA | −10.1532 | −9.303533 | 1.93239 | 4.5498 |
F10 | CPSSA | −10.1532 | −10.15228 | 4.10674 | 2.72803 |
F11 | PSO | −10.40248 | −10.20967 | 0.967761 | 1.85183 |
F11 | ABC | −10.40294 | −10.40294 | 6.41315 | 17.505625 |
F11 | SSA | −10.40294 | −10.04859 | 1.34853 | 4.859621 |
F11 | CPSSA | −10.40282 | −10.40282 | 7.49301 | 3.028603 |
F12 | PSO | −10.53628 | −10.51372 | 0.0370912 | 2.2475 |
F12 | ABC | −10.53641 | −10.53641 | 1.75039 | 18.31669 |
F12 | SSA | −10.53641 | −10.17588 | 1.37204 | 5.470448 |
F12 | CPSSA | −10.53629 | −10.53629 | 5.21556 | 3.381584 |
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Xiong, Q.; Zhang, X.; He, S.; Shen, J. A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image. Mathematics 2021, 9, 2790. https://doi.org/10.3390/math9212790
Xiong Q, Zhang X, He S, Shen J. A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image. Mathematics. 2021; 9(21):2790. https://doi.org/10.3390/math9212790
Chicago/Turabian StyleXiong, Qi, Xinman Zhang, Shaobo He, and Jun Shen. 2021. "A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image" Mathematics 9, no. 21: 2790. https://doi.org/10.3390/math9212790
APA StyleXiong, Q., Zhang, X., He, S., & Shen, J. (2021). A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image. Mathematics, 9(21), 2790. https://doi.org/10.3390/math9212790