Abstract
In order to yield satisfied image after denosing processing, the process of error tracing is nearly necessary for parameter selection. In practice, usually the choice of such parameters is time consuming and empirically dependant when a ground-truth reference is inavailable. Although some successful methods have been proposed in recent research, they still require certain parameters to be set a priori for parameter optimization. These methods tend to be strongly reliant on restrictive assumptions on the signal and noise of input image. In this paper, we propose a framework of parameter selection, which is based on subjective perceptive evaluation and implementated by a well-designed convolutional neural network. The proposed algorithm has the following advantages: (1) consistents with subjective perception, (2) does not require a reference image available, (3) with better capability of robustness and generalization, (4) trims the number of iteration of denosing in parameter selection. Experimental results show that our algorithm outperforms other methods in parameter selection. Our parameter trimming framework saves the computation of iterative image denoising up to 74%.
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
Wang, Z.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004). A Publication of the IEEE Signal Processing Society
Zhang, L.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20, 2378–2386 (2011). A Publication of the IEEE Signal Processing Society
Tao, D.: Reduced-reference IQA in contourlet domain. IEEE Trans. Syst. Man Cybern. Part B Cybern. 39, 1623–1627 (2009). A Publication of the IEEE Systems Man & Cybernetics Society
Doermann, D.: Unsupervised feature learning framework for no-reference image quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1098–1105. IEEE, Providence (2012)
Kang, L., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for no-reference image quality assessment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1733–1740. IEEE Computer Society, Columbu (2014)
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21, 4695–4708 (2012)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 60–65. IEEE Computer Society, San Diego (2005)
Dabov, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007). A Publication of the IEEE Signal Processing Society
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15, 3736–3745 (2006)
Herraiz, J.L., Gabarda, S., Cristobal, G.: Automatic parameter selection in PET image reconstruction based on no-reference image quality assessment. In: Nuclear Science Symposium and Medical Imaging Conference, pp. 3371–3374. IEEE, Anaheim (2012)
Zhu, X., Milanfar, P.: Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Process. 19, 3116–3132 (2010)
Liang, H., Weller, D.S.: Comparison-based image quality assessment for selecting image restoration parameters. IEEE Trans. Image Process. 25, 5118–5130 (2016)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. In: Eleventh International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science: Computational Issues in Nonlinear Science, North-Holland, Los Alamos, pp. 259–268. Elsevier (1992)
Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems. Soc. Ind. Appl. Math. 2, 323–343 (2009)
Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: LIVE image quality assessment database release 2. http://live.ece.utexas.edu/research/quality
Ponomarenko, N.: TID2008 - a database for evaluation of full-reference visual quality assessment metrics. Adv. Mod. Radioelectron. 10, 30–45 (2004)
Larson, E.C., Chandler, D.M.: Most apparent distortion: full-reference image quality assessment and the role of strategy. J. Electron. Imaging 19, 011006 (2010)
Liang, H., Weller, D.S.: Regularization parameter trimming for iterative image reconstruction. In: Asilomar Conference on Signals, Systems and Computers, pp. 755–759. IEEE, Pacific Grove (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Li, J., Xu, L., Li, H., Chang, Cc., Sun, F. (2018). Parameter Selection for Denoising Algorithms Using NR-IQA with CNN. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-73603-7_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73602-0
Online ISBN: 978-3-319-73603-7
eBook Packages: Computer ScienceComputer Science (R0)