본문 바로가기
KERD
논문KCI 등재

Regularization Parameter Selection for Total Variation Model Based on Local Spectral Response

Jin Wang|Jianwei Zhang|Yuhui Zheng|Kai Ma|Qiqiong Yu
한국정보처리학회|2017.10|JIPS(Journal of Information Processing Systems) (Journal of Information Processing Systems)|제13권 제5호|pp. 1168-1182

1

조회

0

다운로드

3

인용

0

참고문헌

키워드

Image DenoisingLocal Spectral ResponseRegularization Parameter Selection

초록

In the past decades, various image regularization methods have been introduced. Among them, total variationmodel has drawn much attention for the reason of its low computational complexity and well-understoodmathematical behavior. However, regularization parameter estimation of total variation model is still an openproblem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposedin this paper, by means of using the local spectral response, which has the capability of locally selecting theregularization parameters in a content-aware way and therefore adaptively adjusting the weights between thetwo terms of the total variation model. Experiment results on simulated and real noisy image show the goodperformance of our proposed method, in visual improvement and peak signal to noise ratio value.

Abstract

In the past decades, various image regularization methods have been introduced. Among them, total variationmodel has drawn much attention for the reason of its low computational complexity and well-understoodmathematical behavior. However, regularization parameter estimation of total variation model is still an openproblem. To deal with this problem, a novel adaptive regularization parameter selection scheme is proposedin this paper, by means of using the local spectral response, which has the capability of locally selecting theregularization parameters in a content-aware way and therefore adaptively adjusting the weights between thetwo terms of the total variation model. Experiment results on simulated and real noisy image show the goodperformance of our proposed method, in visual improvement and peak signal to noise ratio value.

참고문헌 (42)

  1. A. Buades, "A review of image denoising algorithms, with a new one", Multiscale Modeling & Simulation, 4(2), pp.490~530 (2005)
  2. M. Lindenbaum, "On Gabor’s contribution to image enhancement", Pattern Recognition, 27(1), pp.1~8 (1994)
  3. E. Hodson, "Adaptive Gaussian filtering and local frequency estimates using local curvature analysis", IEEE Transactions on Acoustics, Speech, and Signal Processing, 29(4), pp.854~859 (1981)
  4. P. Perona, "Scale-space and edge detection using anisotropic diffusion", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), pp.629~639 (1990)
  5. G. Gerig, "Nonlinear anisotropic filtering of MRI data", IEEE Transactions on Medical Imaging, 11(2), pp.221~232 (1992)
  6. K. Hildebrandt, "Anisotropic filtering of non-linear surface features", Computer Graphics Forum, 23(3), pp.391~400 (2010)
  7. L. Alvarez, "Image selective smoothing and edge detection by nonlinear diffusion. II", SIAM Journal on Numerical Analysis, 29(3), pp.845~866 (1992)
  8. L. I. Rudin, "Nonlinear total variation based noise removal algorithms", Physica D: Nonlinear Phenomena, 60(1-4), pp.259~268 (1992)
  9. J. F. Giovannelli, "Regularization and Bayesian Methods for Inverse Problems in Signal and Image Processing" (2015), Wiley
  10. S. Osher, "An iterative regularization method for total variation-based image restoration", Multiscale Modeling & Simulation, 4(2), pp.460~489 (2005)
  11. W. Hao, "Alternating total variation and non-local total variation for fast compressed sensing magnetic resonance imaging", Electronics Letters, 51(22), pp.1740~1742 (2015)
  12. L. P. Yaroslavsky, "Digital picture processing : an introduction", Applied Optics, 25(18), pp.3127~ (1985)
  13. R. C. Gonzalez, "Digital Image Processing" (2011), Publishing House of Electronics Industry
  14. M. K. Ozkan, "Efficient multiframe Wiener restoration of blurred and noisy image sequences", IEEE Transactions on Image Processing, 1(4), pp.453~476 (1992)
  15. S. Citrin, "A full-plane block Kalman filter for image restoration", IEEE Transactions on Image Processing, 1(4), pp.488~495 (1992)
  16. R. Peesapati, "Efficient hybrid Kalman filter for denoising fiber optic gyroscope signal", Optik-International Journal for Light and Electron Optics, 124(20), pp.4549~4556 (2013)
  17. A. Buades, "A non-local algorithm for image denoising", Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.60~65 (2005)
  18. Y. Zheng, "An improved fast nonlocal means filter using patch-oriented 2DPCA", International Journal of Hybrid Information Technology, 5(3), pp.33~40 (2012)
  19. Z. Yang, "Nonlocal regularization of inverse problems : a unified variational framework", IEEE Transactions on Image Processing, 22(8), pp.3192~3203 (2013)
  20. Y. Lou, "Image recovery via nonlocal operators", Journal of Scientific Computing, 42(2), pp.185~197 (2010)
  21. B. Xue, "Fast nonlocal remote sensing image denoising using cosine integral images", IEEE Geoscience and Remote Sensing Letters, 10(6), pp.1309~1313 (2013)
  22. C. Zhang, "Non-local regularized variational model for image deblurring under mixed Gaussian-impulse noise", Journal of Internet Technology, 16(7), pp.1301~1319 (2015)
  23. M. Elad, "Why simple shrinkage is still relevant for redundant representations", IEEE Transactions on Information Theory, 52(12), pp.5559~5569 (2006)
  24. E. Le Pennec, "Sparse geometric image representations with bandelets", IEEE Transactions on Image Processing, 14(4), pp.423~438 (2005)
  25. M. Elad, "Image denoising via sparse and redundant representations over learned dictionaries", IEEE Transactions on Image Processing, 15(12), pp.3736~3745 (2006)
  26. J. Yang, "Image super-resolution via sparse representation", IEEE Transactions on Image Processing, 19(11), pp.2861~2873 (2010)
  27. T. Peleg, "Exploiting statistical dependencies in sparse representations for signal recovery", IEEE Transactions on Signal Processing, 60(5), pp.2286~2303 (2012)
  28. J. Ren, "Context-aware sparse decomposition for image denoising and superresolution", IEEE Transactions on Image Processing, 22(4), pp.1456~1469 (2013)
  29. J. M. Bioucas-Dias, "A new TwIST : two-step iterative shrinkage", thresholding algorithms for image restoration, 2007, pp.16(12), 2992~3004
  30. Y. Wang, "A new alternating minimization algorithm for total variation image reconstruction", SIAM Journal on Imaging Sciences, 1(3), pp.248~272 (2008)
  31. J. Yang, "A fast alternating direction method for TVL1-L2 signal reconstruction from partial Fourier data", IEEE Journal of Selected Topics in Signal Processing, 4(2), pp.288~297 (2010)
  32. K. Chen, "An automatic regularization parameter selection algorithm in the total variation model for image deblurring", Numerical Algorithms, 67(1), pp.73~92 (2014)
  33. Y. W. Wen, "Parameter selection for total-variation-based image restoration using discrepancy principle", IEEE Transactions on Image Processing, 21(4), pp.1770~1781 (2012)
  34. N. P. Galatsanos, "Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation", IEEE Transactions on Image Processing, 1(3), pp.322~336 (1992)
  35. Q. Yuan, "Adaptive multiple-frame image super-resolution based on Ucurve", IEEE Transactions on Image Processing, 19(12), pp.3157~3170 (2010)
  36. Y. Zheng, "Adaptively determining regularisation parameters in nonlocal total variation regularisation for image denoising", Electronics Letters, 51(2), pp.144~145 (2015)
  37. V. Estellers, "Adaptive regularization with the structure tensor", IEEE Transactions on Image Processing, 24(6), pp.1777~1790 (2015)
  38. G. Gilboa, "Variational denoising of partly textured images by spatially varying constraints", IEEE Transactions on Image Processing, 15(8), pp.2281~2289 (2006)
  39. G. Gilboa, "A total variation spectral framework for scale and texture analysis", SIAM Journal on Imaging Sciences, 7(4), pp.1937~1961 (2014)
  40. Y. Zheng, "Advanced Multimedia and Ubiquitous Engineering", pp.640~645 (2017), Springer
  41. J. Zhang, "Gaussian mixture model learning based image denoising method with adaptive regularization parameters", Multimedia Tools and Applications, 76(9), pp.11471~11483 (2017)
  42. J. Zhang, "Regularization parameter selection for TV image denoising using spatially adaptive local spectral response", Journal of Internet Technology, 17(6), pp.1117~1124 (2016)

관련 논문AI추천

A Windowed-Total-Variation Regularization Constraint Model for Blind Image RestorationGanghua Liu, Juncheng Zou, Shu Tang, Wei Tian, Yushun Luo한국정보처리학회 · JIPS(Journal of Information Processing Systems)2022 · 제18권 제1호Image Global K-SVD Variational Denoising Method Based on Wavelet TransformChang Wang, Wen Zhang한국정보처리학회 · JIPS(Journal of Information Processing Systems)2023 · 제19권 제3호Iterative reweighted algorithm for non-convex Poissonian image restoration model윤상운, 정윤모, 정태욱대한수학회 · 대한수학회지2018 · 제55권 제3호Modified Weighted Filter Algorithm for Noise Elimination in Mixed Noise Environments김남호한국융합신호처리학회 · 융합신호처리학회 논문지2012 · 제13권 제2호An Improved Level Set Method to Image Segmentation Based on SaliencyXianfa Xu, Yan Wang한국정보처리학회 · JIPS(Journal of Information Processing Systems)2019 · 제15권 제1호Image Denoising via Fast and Fuzzy Non-local Means AlgorithmJunrui Lv, Xuegang Luo한국정보처리학회 · JIPS(Journal of Information Processing Systems)2019 · 제15권 제5호

이 간행물의 다른 논문 (25건)

Efficient Approaches to Computer Vision and Pattern Recognition박종혁pp. 1043-1051A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural NetworkZilong Jiang, Shu Gao, Wei Daipp. 1052-1070Outage Performance of Selective Dual-Hop MIMO Relaying with OSTBC and Transmit Antenna Selection in Rayleigh Fading Channels이호원, 최현호, 이인호pp. 1071-1088Test Set Generation for Pairwise Testing Using Genetic AlgorithmsManuj Aggarwal, Sangeeta Sabharwalpp. 1089-1102Extraction of ObjectProperty-UsageMethod Relation from Web DocumentsChaveevan Pechsiri, Sumran Phainoun, Rapeepun Piriyakulpp. 1103-1125
원문 보기