(48-3) 14 * << * >> * Russian * English * Content * All Issues

Noise filtering method in images in sparse-view covers
Y.V. Goshin 1, D.V. Arkhipova 1

1 Samara National Research University,
443086, Samara, Russia, Moskovskoye Shosse 34

 PDF, 2075 kB

DOI: 10.18287/2412-6179-CO-1412

Pages: 432-438.

Full text of article: Russian language.

Abstract:
This article describes a method for image restoration using sparse representations. A sparse representation is a description of an image in the form of fragments of coefficients selected from a predefined dictionary. This paper presents a general approach to image restoration using sparse representation and pre-prepared experimental results for a simple implementation of this convention.

Keywords:
sparse coding, denoising methods.

Citation:
Goshin Y, Arkhipova D. Noise filtering method in images in sparse-view covers. Computer Optics 2024; 48(3): 432-438. DOI: 10.18287/2412-6179-CO-1412.

Acknowledgements:
The work was funded from a government project FSSS-2023-0006.

References:
  1. Fang L, Zhang P. A review on the research of classical image denoising algorithm. Industrial Control Computers 2010; 23: 73-74.
  2. Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. 2005 IEEE Computer Society Conf on Computer Vision and Pattern Recognition (CVPR'05) 2005; 2: 60-65.
  3. Kostenko PYu, Vasylishin VI, Slobodyanyuk VV. Reducing additive noise in digital images using surrogate data technology [In Russian]. Sistemy Obrabotki Informacii 2014; 8: 33-38.
  4. Bardin BV. Fast median filtering algorithm [In Russian]. Nauchnoe Priborostroenie 2011; 21(3):135-139.
  5. Ognev IV, Sidorova NA. Image processing using mathematical morphology methods in an associative oscillatory environment [In Russian]. Izvestiya Vysshikh Uchebnykh Zavedeniy. Povolzhskiy Region. Tekhnicheskie Nauki 2007; 4: 87-97.
  6. Kalinichenko YuV, Ishchenko YaS. Fast Gaussian blur algorithm [In Russian]. Collection of scientific works SWORLD 2012; 4: 83.
  7. Rekhviashvili SSh. Application of wavelet transform for image processing in an atomic force microscope [In Russian]. Pis'ma v ZHTF 2002; 28(6): 46-50.
  8. Lukin AS, Kalinkina DA. Using a combination of principal component analysis and wavelet transform to reduce noise in images [In Russian]. Int Conf of Undergraduate and Postgraduate Students in Basic Sciences “Lomonosov 2005” 2005.
  9. Kesareva ED. Anisotropic diffusion in noise reduction problems [In Russian]. Conf “Voprosy Obrazovaniya i Nauki: Teoreticheskiy i Metodicheskiy Aspekty”. Collection of scientific works 2015; 6(1): 99-101.
  10. Huang M, Huang WQ, Li JB. Research on parameters based on BM3D image denoising algorithm. Industrial Control Computers 2014; 10: 99-101.
  11. Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans Image Process 2007; 16(8): 2080-2095.
  12. Dong W, Zhang L, Shi G, Li X. Nonlocally centralized sparse representation for image restoration. IEEE Trans Image Process 2012; 22(4): 1620-1630.
  13. Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 2006; 15(12): 3736-3745.
  14. Schmidt U, Roth S. Shrinkage fields for effective image restoration. Proc IEEE Conf on Computer Vision and Pattern Recognition 2014: 2774-2781.
  15. Chen Y, Pock T. Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 2016; 39(6): 1256-1272.
  16. Lucas A, Iliadis M, Molina R, Katsaggelos AK. Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Process Mag 2018; 35(1): 20-36.
  17. Arkhipova DV, Goshin EV. Sparse representation algorithm in the problem of image denoising [In Russian]. The VIII Int Conf on Information Technology and Nanotechnology (ITNT-2022) 2022; 3: 33413.
  18. Arkhipova DV, Goshin EV. Study of denoising method based on sparse representation [In Russian]. The IX Int Conf on Information Technology and Nanotechnology (ITNT-2023) 2023; 3: 32742.
  19. Donoho DL, Johnstone JM. Ideal spatial adaptation by wavelet shrinkage. Biometrika 1994; 81(3): 425-455.
  20. Shin DH, et al. Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans Consum Electron 2005; 51(1): 218-226.
  21. Liu W, Lin W. Additive white Gaussian noise level estimation in SVD domain for images. IEEE Trans Image Process 2012; 22(3): 872-883.
  22. Mallat S. A wavelet tour of signal processing: the sparse way. Philadelphia: Elsevier; 2009. ISBN: 978-0-12-374370-1.
  23. Elad M. Sparse and redundant representations. New York: Springer; 2010. ISBN: 978-1441970107.
  24. Elad M, Figueiredo MAT, Ma Y. On the role of the sparse and redundant representations in image processing. Proc IEEE 2010; 98(6): 972-982. DOI: 10.1109/JPROC.2009.2037655.
  25. Zhang S, et al. Shape prior modeling using sparse representation and online dictionary learning. Med Image Comput Comput Assist Interv 2012; 15(3): 435-442.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20