Abstract
To minimize image blurring and detail loss caused by denoising, we propose a novel method to exploit residual image. Firstly, we apply Non-local Means (NLM) filter to original image to get the denoised image and store the weights used for averaging. Secondly, we filter the residual image with the stored weights. Then a Gaussian filter is applied to the denoised residual image before we add the results to image denoised by NLM to recover the lost image details. Different from previous methods, our method uses the structure information in the original image and can be used to extract lost image details from residual images with very low SNR. An analysis on the mechanism of the signal extraction method is given. Quantitative evaluation showed that the proposed algorithm effectively improved accuracy of NLM filter. In addition, the residual of the final results contained fewer observable structures, demonstrating the effectiveness of the proposed method to recover lost details.
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
Preview
Unable to display preview. Download preview PDF.
References
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Transactions on Graphics 27(3) (2008)
Brunet, D., Vrscay, E.R., Wang, Z.: The Use of Residuals in Image Denoising. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 1–12. Springer, Heidelberg (2009)
Manjon, J.V., et al.: MRI denoising using non-local means. Med. Image Anal. 12(4), 514–523 (2008)
Buades: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. (2005)
Chuang, K.S., Huang, H.K.: Assessment of noise in a digital image using the join-count statistic and the Moran test. Phys. Med. Biol. 37(2), 357–369 (1992)
Osher, S., Burger, M., Goldfarb, D., Xu, J.J., Yin, W.T.: An iterative regularization method for total variation-based image restoration. Multiscale Modeling & Simulation 4(2), 460–489 (2005)
Rajwade, A., Rangarajan, A., Banerjee, A.: Automated Filter Parameter Selection using Measures of Noiseness. In: Canadian Conference Computer and Robot Vision, pp. 86–93 (2010)
Jeng, F.-C., Woods, J.W.: Inhomogeneous gaussian image models for estimation and restoration. IEEE Trans. Acoust., Speech, Signal Proc. 36(8), 1305–1312 (1988)
Coupé, P., Yger, P., Barillot, C.: Fast non local means denoising for 3D MR images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 33–40. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, MX., Yan, X., Xie, HB., Zheng, H., Yang, G. (2013). An Effective Method for Signal Extraction from Residual Image, with Application to Denoising Algorithms. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_79
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)