Abstract
Digital images are often corrupted by additive noises during transmission. Thus, how to alleviate noise as much as possible has received concerns for decades. In this paper, we present a simple denoising method based on two dimensional (2-D) finite impulse response (FIR) filtering, where by differential evolution particle swarm optimization (DEPSO) algorithm, five two dimensional finite impulse response filters are designed to filter different kinds of pixels. Comprised by differential evolution algorithm and particle swarm optimization algorithm, differential evolution particle swarm optimization algorithm is effective and robust, which helps to yield better denoise performance. And computer simulation demonstrates that the proposed method is superior to the conventional lowpass filtering method, as well as the modern bilateral filtering and stochastic denoising method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Chen Y, Yan G (2006) Optimum design of 2-D lowpass FIR filters for image processing based on a new algorithm. In: Proceedings of the 25th Chinese control conference, pp 1110–1103
Estrada F, Fleet D, Jepson A (2009) Stochastic image denoising. In: British machine vision conference. http://www.cs.utoronto.ca/~strider/Denoise/. Accessed 20 Nov 2011
Gonzalez RC, Woods RE (2008) Digital image processing. Prentice Hall, Englewood Cliffs, NJ
Hao ZF, Guo GH, Huang H (2007) A particle swarm optimization algorithm with differential evolution. In: International conference on machine learning and cybernetics, vol 2, pp 1031–1035
Lim JS (1990) Two-dimensional signal and image processing. Prentice Hall, Englewood Cliffs, NJ
Potnis A, Somkuwar A, Sapre SD (2010) A review on natural image denoising using independent component analysis (ica) technique. Adv Comput Res 2(1):6–14
Storn R, Price K (1995) Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI Technical Report TR-95-012. http://www.icsi.berkeley.edu/~storn/TR-95-012.pdf. Accessed 16 Aug 2011
Terrell TJ, Simpson RJ (1986) Two-dimensional FIR filter for digital image processing. J Inst Electron Radio Eng 56(3):103–106
Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: International conference on computer vision, pp 839–846
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. http://www.ece.uwaterloo.ca/~z70wang/research/ssim/. Accessed 20 Nov 2011
Yin L, Yang R, Gabbouj M, Neuvo Y (1996) Weighted median filters: a tutorial. IEEE Trans Circuits Syst, II Analog Digit Signal Process 43(3):157–192
Zhang S (2011) Image denoising using FIR filters designed with evolution strategies. In: 2011 3rd international workshop on intelligent systems and applications (ISA), pp 1–4
Zhang W, Xie X (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: IEEE international conference on systems, man and cybernetics, vol 4, pp 3816–3821
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the key project of Chinese ministry of education under grant No.210087, Zhejiang provincial science & technology project (No.2012R10011-6) and in part by the open research fund of national mobile communications research laboratory, Southeast University (No.2010D06).
Rights and permissions
About this article
Cite this article
Hua, J., Kuang, W., Gao, Z. et al. Image denoising using 2-D FIR filters designed with DEPSO. Multimed Tools Appl 69, 157–169 (2014). https://doi.org/10.1007/s11042-012-1263-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-012-1263-1