Skip to main content
Log in

An improved image denoising technique using differential evolution-based salp swarm algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper proposes an improved denoising method based on the cascaded arrangement of filters. The different combinations of filters are obtained optimally through the improved performance of the salp swarm algorithm and cascading four filters out of twelve different types of filters. The searching ability of standard salp swarm algorithm is enhanced following the strategies in differential evolution, and hence the algorithm is named as differential evolution-based salp swarm algorithm (DESSA). Most of the existing image denoising algorithms are suitable to remove either Gaussian, Salt & Pepper, or Speckle noise. Alternatively, due to the optimal combination of filters in the cascaded arrangement, the proposed denoising method exhibits its effectiveness in the removal of all three noises and the denoised images are better in terms of both quantitative analysis and visual quality. The denoising performance of the proposed method is also tested on the mixed noise which demonstrates the significant improvements compared to state-of-the-art algorithms. Further, the experiment on CEC 2014 benchmark functions indicates that the proposed DESSA achieves better optimal solutions than existing algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Aggarwal HK, Majumdar A (2015) Mixed gaussian and impulse denoising of hyperspectral images. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 429–432

  • Aljarah I, Mafarja M, Heidari AA, Faris H, Zhang Y, Mirjalili S (2018) Asynchronous accelerating multi-leader salp chains for feature selection. Appl Soft Comput 71:964–979

    Google Scholar 

  • Arias-Castro E, Salmon J, Willett R (2011) Oracle inequalities and minimax rates for nonlocal means and related adaptive kernel-based methods. Siam Journal on Imaging Sciences \(-\) SIAM J IMAGING SCI 5

  • Ashour AS, Beagum S, Dey N, Ashour AS, Pistolla DS, Nguyen GN, Le DN, Shi F (2018) Light microscopy image de-noising using optimized lpa-ici filter. Neural Comput Appl 29(12):1517–1533

    Google Scholar 

  • Baygi SMH, Karsaz A, Elahi A (2018) A hybrid optimal pid-fuzzy control design for seismic exited structural system against earthquake \(:\) a salp swarm algorithm. In: 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp 220–225

  • Bhandari AK, Kumar A, Singh GK, Soni V (2016) Performance study of evolutionary algorithm for different wavelet filters for satellite image denoising using sub-band adaptive threshold. J Exp Theor Artif Intell 28(1–2):71–95

    Google Scholar 

  • Blu T, Luisier F (2007) The sure-let approach to image denoising. IEEE Trans Image Process 16(11):2778–2786

    MathSciNet  Google Scholar 

  • Chandra A, Chattopadhyay S (2016) A new strategy of image denoising using multiplier-less fir filter designed with the aid of differential evolution algorithm. Multimed Tools Appl 75(2):1079–1098

    Google Scholar 

  • Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 9(9):1532–1546

    MathSciNet  MATH  Google Scholar 

  • Chaudhury KN, Rithwik K (2015) Image denoising using optimally weighted bilateral filters \(:\) A sure and fast approach. CoRR arXiv:1505.00074

  • Chaudhury KN, Dabhade SD (2016) Fast and provably accurate bilateral filtering. IEEE Trans Image Process 25(6):2519–2528

    MathSciNet  MATH  Google Scholar 

  • Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095

    MathSciNet  Google Scholar 

  • de Paiva JL, Toledo CFM, Pedrini H (2016) An approach based on hybrid genetic algorithm applied to image denoising problem. Appl Soft Comput 46:778–791

    Google Scholar 

  • Deledalle CA, Duval V, Salmon J (2011) Non-local methods with shape-adaptive patches (nlm-sap). J Math Imaging Vis 43:103–120

    MathSciNet  MATH  Google Scholar 

  • Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106

    Google Scholar 

  • Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627

    MathSciNet  MATH  Google Scholar 

  • Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224

    MathSciNet  MATH  Google Scholar 

  • Ekinci S, Hekimoglu B (2018) Parameter optimization of power system stabilizer via salp swarm algorithm. In: 5th International Conference on Electrical and Electronic Engineering (ICEEE), pp 143–147

  • El-Fergany AA (2018) Extracting optimal parameters of pem fuel cells using salp swarm optimizer. Renew Energy 119:641–648

    Google Scholar 

  • Erkan U, Gokrem L, Enginoglu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng 70:789–798

    Google Scholar 

  • Eslami R, Radha H (2003) The contourlet transform for image denoising using cycle spinning. In: The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, 2003, vol 2, pp 1982–1986

  • Fajardo-Delgado D, Sanchez MG, Molinar-Solis JE, Fernandez-Zepeda JA, Vidal V, Verdiu G (2016) A hybrid genetic algorithm for color image denoising. In: IEEE Congress on Evolutionary Computation (CEC), pp 3879–3886

  • Faris H, Mafarja MM, Heidari AA, Aljarah I, Al-Zoubi AM, Mirjalili S, Fujita H (2018) An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowl-Based Syst 154:43–67

    Google Scholar 

  • Frost VS, Stiles JA, Shanmugan KS, Holtzman JC (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell 4(2):157–166

    Google Scholar 

  • Guo Q, Yu S, Chen X, Liu C, Wei W (2009) Shearlet-based image denoising using bivariate shrinkage with intra-band and opposite orientation dependencies. Int Joint Conf Comput Sci Optim 1:863–866

    Google Scholar 

  • Gupta V, Chan CC, Sian PT (2007) A differential evolution approach to pet image de-noising. In: 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp 4173–4176

  • Hassan H, Saparon A (2011) Still image denoising based on discrete wavelet transform. In: IEEE International Conference on System Engineering and Technology, pp 188–191

  • He K, Sun J, Tang X (2010) Guided image filtering. Computer vision-ECCV 2010. Springer, Berlin Heidelberg, pp 1–14

    Google Scholar 

  • He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Google Scholar 

  • Hua J, Kuang W, Gao Z, Meng L, Xu Z (2014) Image denoising using 2-d fir filters designed with depso. Multimed Tools Appl 69(1):157–169

    Google Scholar 

  • Hussien AG, Hassanien AE, Houssein EH (2017) Swarming behaviour of salps algorithm for predicting chemical compound activities. In: Eighth International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, pp 315–320

  • Ibrahim A, Ahmed A, Hussein S, Hassanien AE (2018) Fish image segmentation using salp swarm algorithm. In: The International Conference on Advanced Machine Learning Technologies and Applications. Advances in Intelligent Systems and Computing, vol 723, pp 42–51

  • Kaur L, Gupta S, Chauhan RC (2002) Image denoising using wavelet thresholding. In: Indian Conference on Computer Vision, Graphics and Image Processing, Ahmedabad

  • Kockanat S, Karaboga N, Koza T (2012) Image denoising with 2-d fir filter by using artificial bee colony algorithm. In: International Symposium on Innovations in Intelligent Systems and Applications, pp 1–4

  • Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell 7(2):165–177

    Google Scholar 

  • Kumar SV, Nagaraju C (2018) Ffbf: cluster-based fuzzy firefly bayes filter for noise identification and removal from grayscale images. Cluster Computing

  • Lahmiri S (2017) An iterative denoising system based on wiener filtering with application to biomedical images. Optics Laser Technol 90:128–132

    Google Scholar 

  • Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2(2):165–168

    Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the cec 2014 special session and competition on single objective real-parameter numerical optimization. Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University, Singapore

  • Lim W (2010) The discrete shearlet transform: a new directional transform and compactly supported shearlet frames. IEEE Trans Image Process 19(5):1166–1180

    MathSciNet  MATH  Google Scholar 

  • Liu J, Wang Y, Su K, He W (2016) Image denoising with multidirectional shrinkage in directionlet domain. Signal Process 125:64–78

    Google Scholar 

  • Luisier F, Blu T (2008) Sure-let multichannel image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 17(4):482–492

    MathSciNet  Google Scholar 

  • Malik M, Ahsan F, Mohsin S (2016) Adaptive image denoising using cuckoo algorithm. Soft Comput 20(3):925–938

    Google Scholar 

  • Mirjalili S (2016a) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073

    MathSciNet  Google Scholar 

  • Mirjalili S (2016b) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Google Scholar 

  • Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Google Scholar 

  • Mishra S, Bisoi R (2015) Image denoising using neural network based accelerated particle swarm optimization. In: IEEE Power, Communication and Information Technology Conference (PCITC), pp 901–904

  • Muneeswaran V, Rajasekaran MP (2017) Analysis of particle swarm optimization based 2d fir filter for reduction of additive and multiplicative noise in images. In: Theoretical Computer Science and Discrete Mathematics, Springer International Publishing

  • Pham TD (2015) Estimating parameters of optimal average and adaptive wiener filters for image restoration with sequential gaussian simulation. IEEE Signal Process Lett 22(11):1950–1954

    Google Scholar 

  • Rasti B, Ghamisi P, Benediktsson JA (2020) Hyperspectral mixed gaussian and sparse noise reduction. IEEE Geosci Remote Sens Lett 17(3):474–478

    Google Scholar 

  • Rizk-Allah RM, Hassanien AE, Elhoseny M, Gunasekaran M (2018) A new binary salp swarm algorithm \(:\) development and application for optimization tasks. Neural Comput Appl

  • Sayed GI, Khoriba G, Haggag MH (2018) A novel chaotic salp swarm algorithm for global optimization and feature selection. Appl Intell 48(10):3462–3481

    Google Scholar 

  • Sereshki AB, Derakhshani A (2019) Optimizing the mechanical stabilization of earth walls with metal strips: applications of swarm algorithms. Arab J Sci Eng 44(5):4653–4666

    Google Scholar 

  • Shanthi SA, Sulochana CH, Latha T (2015) Image denoising in hybrid wavelet and quincunx diamond filter bank domain based on gaussian scale mixture model. Comput Electr Eng 46:384–393

    Google Scholar 

  • Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684

    MathSciNet  MATH  Google Scholar 

  • Storn R (1996) On the usage of differential evolution for function optimization. In: Proceedings of North American Fuzzy Information Processing, pp 519–523

  • Sun ZX, Hu R, Qian B, Liu B, Che GL (2018) Salp swarm algorithm based on blocks on critical path for reentrant job shop scheduling problems. In: Intelligent Computing Theories and Application, Springer International Publishing, pp 638–648

  • Suresh S, Lal S, Chen C, Celik T (2018) Multispectral satellite image denoising via adaptive cuckoo search-based wiener filter. IEEE Trans Geosci Remote Sens 56(8):4334–4345

    Google Scholar 

  • Toledo CFM, Oliveira LD, Silva RDD, Pedrini H (2013) Image denoising based on genetic algorithm. In: IEEE Congress on Evolutionary Computation, pp 1294–1301

  • Treece G (2016) The bitonic filter: linear filtering in an edge-preserving morphological framework. IEEE Trans Image Process 25(11):5199–5211

    MathSciNet  MATH  Google Scholar 

  • Yang XS (2012) Flower pollination algorithm for global optimization. Unconv Comput Nat Comput Lect Notes Comput Sci 7445:240–249

    MATH  Google Scholar 

  • Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  • Yang HY, Wang XY, Niu PP, Liu YC (2014) Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Netw 57:152–165

    Google Scholar 

  • Youlian Z, Cheng H (2012) Image denoising algorithm based on pso optimizing structuring element. In: 2012 24th Chinese Control and Decision Conference (CCDC), pp 2404–2408

  • Zeng H, Liu YZ, Fan YM, Tang X (2012) An improved algorithm for impulse noise by median filter. AASRI Procedia 1:68–73, aASRI Conference on Computational Intelligence and Bioinformatics

  • Zhang J, Lin G, Wu L, Cheng Y (2016) Speckle filtering of medical ultrasonic images using wavelet and guided filter. Ultrasonics 65:177–193

    Google Scholar 

  • Zhou Y, Lin M, Xu S, Zang H, He H, Li Q, Guo J (2016) An image denoising algorithm for mixed noise combining nonlocal means filter and sparse representation technique. J Vis Commun Image Represent 41:74–86

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Supriya Dhabal.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dhabal, S., Chakrabarti, R., Mishra, N.S. et al. An improved image denoising technique using differential evolution-based salp swarm algorithm. Soft Comput 25, 1941–1961 (2021). https://doi.org/10.1007/s00500-020-05267-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05267-y

Keywords

Navigation