Abstract
Additive noise is one among the prominent types of noises which degrades the quality of images. A very large number of algorithms, in spatial, frequency and wavelet domain have been proposed to enhance images corrupted with additive noise. All the methods suggested have their own advantages as well as disadvantages. With the availability of parallel processing capability, in low end workstations and systems, fusion of two or more de-noising methods has become a topic of interest. In this paper, we have implemented one of the recent contributions to mean filter - a fuzzy filter. Also, as a complementary filter, the basic Non Local Means filter is implemented. Experiments were carried out by fusing the results obtained through the two filters. The results obtained establish the merit of the fusion approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gonzalez, R., Woods, R.: Digital Image Processing, 3rd edn. Pearson Education Inc., Upper Saddle River (2009)
Buades, A., Coll, B., Morel, J.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4, 490–530 (2005)
Nair, M., Raju, G.: Additive noise removal using a novel fuzzy-based filter. Comput. Electr. Eng. 37, 644–655 (2011)
Lan, R., Zhou, Y., Tang, Y., Chen, C.: Image denoising using non-local fuzzy means. In: 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (2015)
James, A., Dasarathy, B.: Medical image fusion: a survey of the state of the art. Inf. Fusion 19, 4–19 (2014)
Li, M., Dong, Y., Li, J.: Overview of pixel level image fusion algorithm. AMM 519–520, 590–593 (2014)
Anita, S., Moses, C.: Survey on pixel level image fusion techniques. In: 2013 IEEE International Conference ON Emerging Trends in Computing, Communication and Nanotechnology (ICECCN) (2013)
Ganasala, P., Kumar, V.: Multimodality medical image fusion based on new features in NSST domain. Biomed. Eng. Lett. 4, 414–424 (2014)
Gu, L.: Research on multi-sensor data level fusion based on artificial neuron. Chin. J. Mech. Eng. 39, 89 (2003)
Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2015) (2015)
Raju, G., Wahid, F., Shareekhath, K.: Modified non-local means filtering. In: 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) (2015)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition (2010)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)
Acknowledgement
The authors would like to acknowledge the University Grants Commission for the financial support extended under the Major Project Scheme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
G., R., Wahid, F.F., K., S. (2017). Additive Noise Removal by Combining Non Local Means Filtering and a Local Fuzzy Filter – A Fusion Approach. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-5427-3_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-5426-6
Online ISBN: 978-981-10-5427-3
eBook Packages: Computer ScienceComputer Science (R0)