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Volumn 3, Issue 6, 1994, Pages 789-801

Sequential and Parallel Image Restoration: Neural Network Implementations

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; ESTIMATION; ITERATIVE METHODS; MATHEMATICAL MODELS; MATRIX ALGEBRA; NEURAL NETWORKS; OPTIMIZATION; PARALLEL ALGORITHMS; ROBUSTNESS (CONTROL SYSTEMS); SPURIOUS SIGNAL NOISE; VECTORS; WHITE NOISE;

EID: 0028546891     PISSN: 10577149     EISSN: 19410042     Source Type: Journal    
DOI: 10.1109/83.336248     Document Type: Article
Times cited : (29)

References (41)
  • 2
    • 0024057651 scopus 로고
    • Ill-posed problems in early vision
    • Aug.
    • M. Bertero, T. Poggio, and V. Torre, “Ill-posed problems in early vision,” Proc. IEEE, vol. 76, pp. 869–889, Aug. 1988.
    • (1988) Proc. IEEE , vol.76 , pp. 869-889
    • Bertero, M.1    Poggio, T.2    Torre, V.3
  • 3
    • 0026137713 scopus 로고
    • A regularized iterative image restoration algorithm
    • Apr.
    • A. Katsaggelos et al., “A regularized iterative image restoration algorithm,” IEEE Trans. Signal Processing, vol. 39, pp. 914–929, Apr. 1991.
    • (1991) IEEE Trans. Signal Processing , vol.39 , pp. 914-929
    • Katsaggelos, A.1
  • 4
    • 0342709572 scopus 로고
    • Inverse problems in image processing
    • A. Tikhonov and A. Goncharsky, Eds. Moscow: Mir
    • A. Tikhonov, A. Goncharsky, and V. Stepanov, “Inverse problems in image processing,” in Ill-Posed Problems in the Natural Sciences, A. Tikhonov and A. Goncharsky, Eds. Moscow: Mir, 1987, pp. 220–232.
    • (1987) Ill-Posed Problems in the Natural Sciences , pp. 220-232
    • Tikhonov, A.1    Goncharsky, A.2    Stepanov, V.3
  • 5
    • 0000013152 scopus 로고
    • On the statistical analysis of dirty pictures
    • J. Besag, “On the statistical analysis of dirty pictures,” J. R. Stat. Soc. B, vol. 48, pp. 259–302, 1986.
    • (1986) J. R. Stat. Soc. B , vol.48 , pp. 259-302
    • Besag, J.1
  • 7
    • 0025462662 scopus 로고
    • Regularization theory in image restoration—the stabilizing functional approach
    • July
    • N. Karayiannis and A. Venetsanopoulos, “Regularization theory in image restoration—the stabilizing functional approach,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, pp. 1155–1179, July 1990.
    • (1990) IEEE Trans. Acoust., Speech, Signal Processing , vol.38 , pp. 1155-1179
    • Karayiannis, N.1    Venetsanopoulos, A.2
  • 8
    • 0010264747 scopus 로고
    • Bayesian and related methods in image reconstruction from incomplete data
    • H. Stark, Ed. New York: Academic
    • K. M. Hanson, “Bayesian and related methods in image reconstruction from incomplete data,” in Image Recovery: Theory and Applications, H. Stark, Ed. New York: Academic, 1987, pp. 79–125.
    • (1987) Image Recovery: Theory and Applications , pp. 79-125
    • Hanson, K.M.1
  • 10
    • 0010980001 scopus 로고
    • Image reconstruction from limited data. Theory and applications in computerized tomography
    • H. Stark, Ed. New York: Academic
    • B. P. Medoff, “Image reconstruction from limited data. Theory and applications in computerized tomography,” Image Recovery: Theory and Applications, H. Stark, Ed. New York: Academic, 1987, pp. 321–368.
    • (1987) Image Recovery: Theory and Applications , pp. 321-368
    • Medoff, B.P.1
  • 11
    • 0026679070 scopus 로고
    • Image restoration using a modified Hopfield network
    • Jan.
    • J. Paik and A. Katsaggelos, “Image restoration using a modified Hopfield network,” IEEE Trans. Image Processing, vol. 1, pp. 49–63, Jan. 1992.
    • (1992) IEEE Trans. Image Processing , vol.1 , pp. 49-63
    • Paik, J.1    Katsaggelos, A.2
  • 13
    • 0021835689 scopus 로고
    • Neural computation of decisions in optimization problems
    • J. Hopfield and D. Tank, “Neural computation of decisions in optimization problems,” Biolog. Cybernet., vol. 52, pp. 141–152, 1985.
    • (1985) Biolog. Cybernet. , vol.52 , pp. 141-152
    • Hopfield, J.1    Tank, D.2
  • 15
    • 0026186346 scopus 로고
    • Superresolution algorithms for a modified Hopfield neural network
    • July
    • J. Abbiss, B. Brames, and M. Fiddy, “Superresolution algorithms for a modified Hopfield neural network,” IEEE Trans. Signal Processing, vol. 39, pp. 1516–1523, July 1991.
    • (1991) IEEE Trans. Signal Processing , vol.39 , pp. 1516-1523
    • Abbiss, J.1    Brames, B.2    Fiddy, M.3
  • 16
    • 0025657909 scopus 로고
    • Image restoration using the Hopfield network with nonzero autoconnections
    • J. Paik and A. Katsaggelos, “Image restoration using the Hopfield network with nonzero autoconnections,” in Proc. Int. Conf. ASSP-ICASSP (Albuquerque, NM), 1990, pp. 1909–1912.
    • (1990) Proc. Int. Conf. ASSP-ICASSP (Albuquerque, NM) , pp. 1909
    • Paik, J.1    Katsaggelos, A.2
  • 17
    • 3943069265 scopus 로고
    • Hopfield-type neural networks
    • A. Katsaggelos, Ed. New York: Springer Verlag
    • S. Yeh, H. Stark, and M. Sezan, “Hopfield-type neural networks,” in Digital Image Restoration, A. Katsaggelos, Ed. New York: Springer Verlag, 1991, pp. 57–88.
    • (1991) Digital Image Restoration , pp. 57-88
    • Yeh, S.1    Stark, H.2    Sezan, M.3
  • 18
    • 0024053982 scopus 로고
    • Image restoration using a neural network
    • July
    • Y. Zhou et al., “Image restoration using a neural network,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 1141–1151, July 1988.
    • (1988) IEEE Trans. Acoust., Speech, Signal Processing , vol.36 , pp. 1141-1151
    • Zhou, Y.1
  • 19
    • 0026622541 scopus 로고
    • Hopfield network for stereo vision correspondence
    • Jan.
    • N. Nasrabadi and C. Choo, “Hopfield network for stereo vision correspondence,” IEEE Trans. Neural Networks, vol. 3, pp. 5–13, Jan. 1992.
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 5-13
    • Nasrabadi, N.1    Choo, C.2
  • 22
    • 0000162735 scopus 로고
    • Parallel image segmentation using a modified Hopfield network
    • May
    • C. Huang, “Parallel image segmentation using a modified Hopfield network,” Pattern Recognit. Letts., vol. 13, pp. 345–353, May 1992.
    • (1992) Pattern Recognit. Letts. , vol.13 , pp. 345-353
    • Huang, C.1
  • 23
    • 0009819007 scopus 로고
    • Robust segmentation of noisy images using a neural network model
    • May
    • T. Wang, X. Zhuang, and X. Xing, “Robust segmentation of noisy images using a neural network model,” Image, Vision Computing, vol. 10, pp. 233–240, May 1992.
    • (1992) Image, Vision Computing , vol.10 , pp. 233-240
    • Wang, T.1    Zhuang, X.2    Xing, X.3
  • 25
    • 84975634948 scopus 로고
    • Neural networks for computation. Number representation and programing complexity
    • M. Takeda and J. Goodman, “Neural networks for computation. Number representation and programing complexity,” Appl. Optics, vol. 25, pp. 3033–3046, 1986.
    • (1986) Appl. Optics , vol.25 , pp. 3033-3046
    • Takeda, M.1    Goodman, J.2
  • 26
    • 0016552759 scopus 로고
    • A new algorithm in spectral analysis and band-limited extrapolation
    • A. Papoulis, “A new algorithm in spectral analysis and band-limited extrapolation,” IEEE Trans. Circuits, Syst., vol. CAS-22, pp. 735–742, 1975.
    • (1975) IEEE Trans. Circuits, Syst. , vol.CAS-22 , pp. 735-742
    • Papoulis, A.1
  • 27
    • 0024768808 scopus 로고
    • Pyramid implementation of optimal-step conjugate-search algorithms for some low-level vision problems
    • Nov.
    • T. Simchony, R. Chellappa, and Z. Lichtenstein, “Pyramid implementation of optimal-step conjugate-search algorithms for some low-level vision problems,” IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 1408–1425, Nov. 1989.
    • (1989) IEEE Trans. Syst., Man, Cybern. , vol.20 , pp. 1408-1425
    • Simchony, T.1    Chellappa, R.2    Lichtenstein, Z.3
  • 28
    • 0002629270 scopus 로고
    • Maximum likelihood estimation from incomplete data via the EM algorithm
    • A. Dempster, N. Laird, and D. Rubin, “Maximum likelihood estimation from incomplete data via the EM algorithm,” J. R. Stat. Soc. B, vol. 39, pp. 1–38, 1977.
    • (1977) J. R. Stat. Soc. B , vol.39 , pp. 1-38
    • Dempster, A.1    Laird, N.2    Rubin, D.3
  • 29
    • 0004601406 scopus 로고
    • Maximum likelihood identification and restoration of images using the expectation-maximization algorithm
    • A. Katsaggelos, Ed. New York: Springer-Verlag
    • A. Katsaggelos and K. Lay, “Maximum likelihood identification and restoration of images using the expectation-maximization algorithm,” in Digital Image Restoration, A. Katsaggelos, Ed. New York: Springer-Verlag, 1991, pp. 143–176.
    • (1991) Digital Image Restoration , pp. 143-176
    • Katsaggelos, A.1    Lay, K.2
  • 30
    • 0024874979 scopus 로고
    • Blur identification using the expectation-maximization algorithm
    • R. Lagendijk, J. Biemond, and D. Boekee, “Blur identification using the expectation-maximization algorithm,” in Proc. Int. Conf. ASSP-ICASSP, 1989, pp. 1397–1400.
    • (1989) Proc. Int. Conf. ASSP-ICASSP , pp. 1397
    • Lagendijk, R.1    Biemond, J.2    Boekee, D.3
  • 31
    • 0027849249 scopus 로고
    • Simulated tearing: An algorithm for discontinuity preserving visual surface reconstruction
    • June
    • M. Figueiredo and J. Leit ã o “Simulated tearing: An algorithm for discontinuity preserving visual surface reconstruction,” in IEEE Conf. Comput. Vision, Pattern Recognit. (New York), June 1993, pp. 28–33.
    • (1993) IEEE Conf. Comput. Vision, Pattern Recognit. (New York) , pp. 28-33
    • Figueiredo, M.1    Leitão, J.2
  • 32
    • 0026151642 scopus 로고
    • Parallel and deterministic algorithms from MRF's: Surface reconstruction
    • May
    • D. Geiger and F. Girosi, “Parallel and deterministic algorithms from MRF's: Surface reconstruction,” IEEE Trans. Pattern Anal. Machine Intell., vol. 13, pp. 401–412, May 1991.
    • (1991) IEEE Trans. Pattern Anal. Machine Intell. , vol.13 , pp. 401-412
    • Geiger, D.1    Girosi, F.2
  • 36
    • 0026838051 scopus 로고
    • Recursive structure of noncausal Gauss-Markov random fields
    • Mar.
    • J. Moura and N. Balram, “Recursive structure of noncausal Gauss-Markov random fields,” IEEE Trans. Inform. Theory, vol. 38, pp. 334–354, Mar. 1992.
    • (1992) IEEE Trans. Inform. Theory , vol.38 , pp. 334-354
    • Moura, J.1    Balram, N.2
  • 37
    • 84975609342 scopus 로고
    • Optical implementation of the Hopfield algorithm using correlation
    • A. David and B. Saleh, “Optical implementation of the Hopfield algorithm using correlation,” Appl. Optics, vol. 29, pp. 1063–1064, 1990.
    • (1990) Appl. Optics , vol.29 , pp. 1063-1064
    • David, A.1    Saleh, B.2
  • 38
    • 84975548756 scopus 로고
    • Optical implementation of the Hopfield model
    • N. Farhat et al., “Optical implementation of the Hopfield model,” Appl. Optics, vol. 24, pp. 1469–1475, 1985.
    • (1985) Appl. Optics , vol.24 , pp. 1469-1475
    • Farhat, N.1
  • 39
    • 84975608607 scopus 로고
    • Optical implementation of an associative neural network model with a stochastic process
    • J. Ohta et al., “Optical implementation of an associative neural network model with a stochastic process,” Appl. Optics, vol. 28, pp. 2426–2428, 1985.
    • (1985) Appl. Optics , vol.28 , pp. 2426-2428
    • Ohta, J.1
  • 40
    • 84941518000 scopus 로고
    • Optical information processing based on an associative memory model of neural nets with thresholding and feedback
    • D. Psaltis and N. Farhat, “Optical information processing based on an associative memory model of neural nets with thresholding and feedback,” Appl. Optics, vol. 28, pp. 98–100, 1985.
    • (1985) Appl. Optics , vol.28 , pp. 98-100
    • Psaltis, D.1    Farhat, N.2
  • 41
    • 0026923823 scopus 로고
    • Bayesian estimation of ventricular contours in angiographic images
    • Sept.
    • M. Figueiredo and J. Leitão, “Bayesian estimation of ventricular contours in angiographic images,” IEEE Trans. Med. Imag., vol. 11, pp. 416–429, Sept. 1992.
    • (1992) IEEE Trans. Med. Imag. , vol.11 , pp. 416-429
    • Figueiredo, M.1    Leitão, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.