Skip to main content
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
2.C. S. Anand and J. S. Sahambi, “Pixel dependent automatic parameter selection for image denoising with bilateral filter,” International Journal of Computer Applications 45, 4146 (2012).
3.V. Aurich and J. Weule, “Non-linear Gaussian filters performing edge preserving diffusion,” in Proc. Deutsche Arbeitstgemein. Mustererkennung Symp. Pattern Recognit. (1995), pp. 538545.
4.S. Awate and R. Whitaker, “Higher-order image statistics for unsupervised, information-theoretic, adaptive, image filtering,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2005), Vol.2, pp. 4451.
5.E. Bennett, J. Mason, and L. McMillan, “Multispectral bilateral video fusion,” IEEE Trans. Image Process. 16, 11851194 (2007).
6.J. Bigün and G. H. Granlund, “Optimal orientation detection of linear symmetry,” in Proc. IEEE Int. Conf. on Comp. Vis., London, Great Britain (1987), pp. 433438.
7.T. Blu and F. Luisier, “The SURE-LET approach to image denoising,” IEEE Trans. Image Process. 16, 27782786 (2007), ISSN 1057-7149.
8.A. Buades, B. Coll, and J. M. Morel, “A non-local algorithm for image denoising,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2005), Vol.2, pp. 6065.
9.K. Carlsson, P.-E. Danielsson, A. Liljeborg, L. Majlöf, R. Lenz, and N. Åslund, “Three-dimensional microscopy using a confocal laser scanning microscope,” Optics Letters 10, 5355 (1985).
10.K. Chaudhury, D. Sage, and M. Unser, “Fast O(1) bilateral filtering using trigonometric range kernels,” IEEE Trans. Image Process. 20, 33763382 (2011).
11.J. Chen, C.-K. Tang, and J. Wang, “Noise brush: interactive high quality image-noise separation,” in ACM Transactions on Graphics (2009), Vol. 28, p. 146.
12.Y. Chen and Y. Shu, “Optimization of bilateral filter parameters via chi-square unbiased risk estimate,” IEEE Signal Process. Lett. 21, 97100 (2014).
13.K. Chung, J. Wallace, S.-Y. Kim, S. Kalyanasundaram, A. S. Andalman, T. J. Davidson, J. J. Mirzabekov, K. A. Zalocusky, J. Mattis, A. K. Denisin et al., “Structural and molecular interrogation of intact biological systems,” Nature 497, 332337 (2013).
14.M. Elad, “On the origin of the bilateral filter and ways to improve it,” IEEE Trans. Image Process. 11, 11411151 (2002).
15.M. Elad, “Retinex by two bilateral filters,” in Proc. Scale-Space Conf. (2005), pp. 217229.
16.A. Fine, W. Amos, R. Durbin, and P. McNaughton, “Confocal microscopy: Applications in neurobiology,” Trends in Neuroscience 11, 346351 (1988).
17.H. Hama, H. Kurokawa, H. Kawano, R. Ando, T. Shimogori, H. Noda, K. Fukami, A. Sakaue-Sawano, and A. Miyawaki, “Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain,” Nature Neuroscience 14, 14811488 (2011).
18.H. Hashii, G. Tanaka, N. Suetake, and E. Uchino, “Parameter tuning of bilateral filter based on hellinger distance,” Proc. Fuzzy System Symposium 26, 216 (2010).
19.K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35, 13971409 (2013).
20.S. W. Hell, “Far-field optical nanoscopy,” Science 316, 11531158 (2007).
21.A. Jose and C. S. Seelamantula, “Bilateral edge detectors,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (2013), pp. 14491453.
22.H. Kishan and C. S. Seelamantula, “SURE-fast bilateral filters,” in Proc. IEEE Int. Conf. Acoust., Speech and Signal Process. (2012), pp. 11291132.
23.F. Luisier and P. Wolfe, “Chi-square unbiased risk estimate for denoising magnitude MR images,” in Proc. IEEE Int. Conf. on Image Processing (2011), pp. 15611564.
24.S. Mallat, A Wavelet Tour of Signal Processing (Academic press, 1999).
25.P. Milanfar, “A tour of modern image filtering: New insights and methods, both practical and theoretical,” IEEE Signal Processing Magazine 30, 106128 (2013).
26.M. Minsky, “Microscopy apparatus,” US Patent 3,013,467 (1961).
27.A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process. 21, 46954708 (2012).
28.P. P. Mondal and A. Diaspro, Fundamentals of Fluorescence Microscopy: Exploring Life with Light (Springer Science & Business Media, 2013).
29.M. Muller, Introduction to Confocal Fluorescence Microscopy (SPIE press, 2006), Vol.69.
30.E. N. Olson and A. Nordheim, “Linking actin dynamics and gene transcription to drive cellular motile functions,” Nature Reviews Molecular Cell biology 11, 353365 (2010).
31.S. Paris and F. Durand, “A fast approximation of the bilateral filter using a signal processing approach,” Int. J. Comput. Vision 81, 2452 (2009), ISSN 0920-5691.
32.J. B. Pawley, Handbook of Biological Confocal Microscopy (Springer, New York (N.Y.), 2006), ISBN: 978-0387-25921-5.
33.H. Peng and R. Rao, “Bilateral kernel parameter optimization by risk minimization,” inProc. IEEE Int. Conf. on Image Process. (2010), pp. 32933296.
34.P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 629639 (1990), ISSN 0162-8828.
35.F. Porikli, “Constant time O(1) bilateral filtering,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2008), pp. 18.
36.R. Ramanath and W. E. Snyder, “Adaptive demosaicking,” J. El. Imag. 12, 633642 (2003).
37.C. M. Stein, “Estimation of the mean of a multivariate normal distribution,” The Annals of Statistics 9, 11351151 (1981).
38.E. H. Stelzer, “Light microscopy: Beyond the diffraction limit?,” Nature 417, 806807 (2002).
39.S. Tojkander, G. Gateva, and P. Lappalainen, “Actin stress fibers–assembly, dynamics and biological roles,” Journal of Cell Science 125, 18551864 (2012).
40.C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. IEEE Int. Conf. on Comp. Vis. (1998), pp. 839846.
41.C.-K. Tung, Y. Sun, W. Lo, S.-J. Lin, S.-H. Jee, and C.-Y. Dong, “Effects of objective numerical apertures on achievable imaging depths in multiphoton microscopy,” Microscopy Research and Technique 65, 308314 (2004).
42.W. Wang, J. Gao, K. Li, K. Ma, and X. Zhang, “Structure-oriented gaussian filter for seismic detail preserving smoothing,” in Proc. IEEE Int. Conf. on Image Process. (2009), pp. 601604, ISSN 1522-4880.
43.Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13, 600612 (2004).
44.J. Xiao, H. Cheng, H. S. Sawhney, C. Rao, and M. A. Isnardi, “Bilateral filtering-based optical flow estimation with occlusion detection,” in Proc. European Conf. on Comp. Vis. (2007), pp. 211224.
45.Q. Yang, K.-H. Tan, and N. Ahuja, “Real-time O(1) bilateral filtering,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2009), pp. 557564.

Data & Media loading...


Article metrics loading...



Images obtained through fluorescence microscopy at low numerical aperture (NA) are noisy and have poor resolution. Images of specimens such as F-actin filaments obtained using confocal or widefield fluorescence microscopes contain directional information and it is important that an image smoothing or filtering technique preserve the directionality. F-actin filaments are widely studied in pathology because the abnormalities in actin dynamics play a key role in diagnosis of cancer, cardiac diseases, vascular diseases, myofibrillar myopathies, neurological disorders, etc. We develop the directional bilateral filter as a means of filtering out the noise in the image without significantly altering the directionality of the F-actin filaments. The bilateral filter is anisotropic to start with, but we add an additional degree of anisotropy by employing an oriented domain kernel for smoothing. The orientation is locally adapted using a structure tensor and the parameters of the bilateral filter are optimized for within the framework of statistical risk minimization. We show that the directional bilateral filter has better denoising performance than the traditional Gaussian bilateral filter and other denoising techniques such as SURE-LET, non-local means, and guided image filtering at various noise levels in terms of peak signal-to-noise ratio (PSNR). We also show quantitative improvements in low NA images of F-actin filaments.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd