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.
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
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
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
Blu T, Luisier F (2007) The sure-let approach to image denoising. IEEE Trans Image Process 16(11):2778–2786
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
Chang SG, Yu B, Vetterli M (2000) Adaptive wavelet thresholding for image denoising and compression. IEEE Trans Image Process 9(9):1532–1546
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
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
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
Deledalle CA, Duval V, Salmon J (2011) Non-local methods with shape-adaptive patches (nlm-sap). J Math Imaging Vis 43:103–120
Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106
Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627
Donoho DL, Johnstone IM (1995) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90(432):1200–1224
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
Erkan U, Gokrem L, Enginoglu S (2018) Different applied median filter in salt and pepper noise. Comput Electr Eng 70:789–798
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
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
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
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
He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409
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
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
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
Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell 2(2):165–168
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
Liu J, Wang Y, Su K, He W (2016) Image denoising with multidirectional shrinkage in directionlet domain. Signal Process 125:64–78
Luisier F, Blu T (2008) Sure-let multichannel image denoising: interscale orthonormal wavelet thresholding. IEEE Trans Image Process 17(4):482–492
Malik M, Ahsan F, Mohsin S (2016) Adaptive image denoising using cuckoo algorithm. Soft Comput 20(3):925–938
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
Mirjalili S (2016b) Sca: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
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
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
Rasti B, Ghamisi P, Benediktsson JA (2020) Hyperspectral mixed gaussian and sparse noise reduction. IEEE Geosci Remote Sens Lett 17(3):474–478
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
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
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
Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684
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
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
Yang XS (2012) Flower pollination algorithm for global optimization. Unconv Comput Nat Comput Lect Notes Comput Sci 7445:240–249
Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343
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
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
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
Author information
Authors and Affiliations
Corresponding author
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
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05267-y