◄homepage◄ Peer
Reviewed Proceedings Papers
[49] P. Arias and M. Nikolova, “Below
the Surface of the Non-Local Bayesian Image Denoising Method”, Scale-Space and
Variational Methods in Computer Vision. Lecture Notes in Computer Science10302,
Springer, 2017, pp. 208--220 (pdf)
[48] J. Fehrenbach, M. Nikolova, G. Steidl, and P. Weiss, ”Bilevel Image Denoising using Gaussianity
tests”. in J.-F. Aujol, M. Nikolova,
N. Papadakis (Eds.) : Scale-Space and Variational Methods in Computer Vision.
Lecture Notes in Computer Science 9087, Springer, Berlin, 2015, pp. 117–128.
[47] J. H. Fitschen, M. Nikolova, F. Pierre, and G. Steidl, ”A Variational Model for Color Assignment”, in J.-F. Aujol, M. Nikolova, N. Papadakis
(Eds.) : Scale-Space and Variational Methods in Computer Vision. Lecture Notes
in Computer Science 9087, Springer, Berlin, 2015, pp. 437–448.
[46] M. Nikolova, "A fast algorithm for exact histogram specification. Simple extension to
colour images", Scale Space and Variational Methods in Computer Vision, June 2013 (pdf).
[45] M. Nikolova, "Either fit to data entries or to locally to prior: the minimizers of
energies with nonsmooth nonconvex data fidelity and regularization ", Scale Space and
Variational Methods in Computer Vision, June 2011.
[44] M. Nikolova, "Should we search for a global minimizer of least squares regularized
with an ℓ0 penalty to get the exact solution of an under
determined linear system?", Scale Space and Variational
Methods in Computer Vision, June 2011.
[43] R. Chan, M. Nikolova and Y.-W. Wen, "A variational
approach for exact histogram specification ", Scale Space and
Variational Methods in Computer Vision, June 2011.
[42] M. Nikolova, "Fast dejittering for
digital video images ", Scale Space and Variational Methods in Computer Vision, Eds. X.-C. Tai, K. Morken, M. Lysaker,
K.-A. Lie, LNCS 5567, Springer, pp. 439-451, 2009. (pdf)
[41] Durand S., J. Fadili and M. Nikolova, "Multiplicative
noise clearing via a variational method involving curvelet coefficients ",
Scale
Space and Variational Methods in Computer Vision, Eds. X.-C. Tai, K. Morken, M. Lysaker, K.-A. Lie,
LNCS 5567, Springer, pp. 282-294,, 2009. (pdf)
[40] F. Malgouyres. and M. Nikolova, "Average
performance of the sparsest approximation in a dictionary ", Int. Workshop
SPARS’09, April 2009. (pdf)
[39] M. Nikolova, "Bounds on
the minimizers of (nonconvex) regularized least-squares", Scale Space and
Variational Methods in Computer Vision, Springer – Lecture notes in
Computer science LNCS 4485, ed. F. Sgallary, A. Murli, N. Paragios, 2007, pp.
496-507.
[38] M.
Nikolova, "Counter-examples for Bayesian MAP
restoration", Scale Space and Variational Methods in Computer Vision,
Springer – Lecture notes in Computer science LNCS 4485, ed. F. Sgallary, A.
Murli, N. Paragios, 2007, pp. 140-152.
[37] M.
Nikolova, "Restoration of edges by minimizing
non-convex cost-functions", IEEE Int. Conf. on Image Processing (ICIP),
vol. II, pp. 786-789, Sept. 2005.
[36] T
Chan T., S. Esedoglu and M. Nikolova, "Finding the Global
Minimum for Binary Image Restoration", IEEE Int. Conf. on Image
Processing (ICIP), vol. I, pp. 121-124, Sept. 2005.
[35] R.
H. Chan, C. Ho, C.W. Leung and M. Nikolova, "Minimization of
detail-preserving regularization functional by
[34] Fu H., M. Ng, M.
Nikolova, J. L. Barlow, W.-K. Ching, "Fast algorithms for ℓ1
norm/mixed ℓ1 and ℓ2 norms for image
restoration”. ICCSA, vol. 4, pp. 843-851, 2005.
[33] Durand S. and M.
Nikolova, "Restoration of wavelet coefficients by minimizing a specially
designed objective function'', IEEE Int. Conf. on Computer Vision / Workshop on
Variational and Level-Set Methods, vol. 2, pp. 145-152, Oct. 2003. (pdf)
[32] M.
Nikolova, ``Minimization of cost-functions with non-smooth data-fidelity terms to
clean impulsive noise'', Int. workshop on Energy Minimization Methods in
Computer Vision and Pattern Recognition, Lecture Notes in Computer
Science, Springer-Verlag, pp. 391-406, 2003.
[31] Kornprobst, P., R.
Peeters, M. Nikolova, R. Deriche, M. Ng and P. Van Hecke. ``A super-resolution
framework for fMRI sequences and its impact on resulting activation
maps'',
Medical Image Computing and Computer-Assisted Intervention (MICCAI),
LNCS 2879, pp. 117-127, 2003. (pdf)
[30] M.
Nikolova, ``Efficient removing of impulsive noise based on an ℓ1-ℓ2
cost-function'', IEEE Int. Conf. on Image Processing (ICIP), vol. 1, pp. 14-17,
Sep. 2003. (pdf)
[29] Deriche, R., P.
Kornprobst, M. Nikolova and Michael Ng. ``Half-quadratic regularization
for MRI image restoration'', IEEE Int. Conf. on Acoustics, Speech and Signal
Processing (ICASSP), vol. VI, pp. 585-588, 2003.
[28] S. Zinger, M. Nikolova, M. Roux
and H. Maitre, ``Rééchantillonnage
de données 3D laser aéroporté en milieu urbain'', Congrès Vision par Ordinateeur ORASIS, pp.
75-82, Mai 2003.
[27] M.
Nikolova and M. Ng, ``Comparison of the main forms of half-quadratic
regularization'', IEEE Int. Conf. on Image Processing(ICIP), vol. 1, pp. 349-352,
Oct. 2002.
[26] S.
Zinger, M. Nikolova, M. Roux and H. Maitre, ``3D resampling for airborne
laser data of urban areas'', Proceedings of ISPRS, vol. XXXIV, n. 3A, pp.
418-423, 2002.
[25] M.
Nikolova. ``Image restoration by minimizing objective functions with non-smooth
data-fidelity terms'', IEEE Int. Conf. on Computer Vision / Workshop on
Variational and Level-Set Methods, pp. 11-18, Jul. 2001.
[24] S.
Durand and M. Nikolova, ``Stability of image restoration by minimizing
regularized objective functions'', IEEE Int. Conf. on Computer Vision / Workshop on
Variational and Level-Set Methods, pp. 73-80, Jul. 2001.
[23] M.
Nikolova, ``Smoothing of outliers in image restoration by minimizing regularized
objective functions with non-smooth data-fidelity terms'', IEEE Int. Conf.
on Image Processing (ICIP), vol. 1, pp. 233-236n Oct. 2001.
[22] M.
Nikolova and M. Ng, ``Fast image reconstruction algorithms combining
half-quadratic regularization and preconditioning'', IEEE Int. Conf. on Image
Processing, vol. 1, pp. 277-280, Oct. 2001.
[21] M.
Nikolova and A. Hero III, ``Segmentation of a road from a
vehicle-mounted imaging radar and accuracy of the estimation'', Proc. of IEEE
Intelligent Vehicles Symposium, pp. 284-289, Oct. 2000.
[20] F.
Alberge, P. Duhamel and M. Nikolova, ``Low cost adaptive algorithm
for blind channel identification and symbol estimation'', EUSIPCO (Finland),
pp. 1549-1552, Sept. 2000. (pdf)
[19] F.
Roullot, A. Herment, I. Bloch, M. Nikolova and E. Mousseaux, ``Regularized
reconstruction of 3D high-resolution magnetic resonance images from
acquisitions of anisotropically degraded resolutions'', 15th
Int. Conf. on Pattern Recognition, vol. 3, pp. 346-349, 2000.
[18] F. Roullot, A. Herment, I. Bloch,
M. Nikolova and E. Mousseaux,
``Reconstruction regularise d’images de resonance magnétique 3D de haute
resolution à partir d’acquisitions anisotropes'', RFIA (Paris, France), vol. II, pp. 59-68, 2000.
[17] M.
Nikolova,
``Assumed and effective priors in Bayesian MAP estimation'', IEEE Int. Conf.
on Acoustics, Speech and Signal Processing(ICASSP), Jun. 2000, vol. 1, pp.
305-308. (pdf)
[16] F.
Alberge, M. Nikolova and P. Duhamel, ``Adaptive Deterministic Maximum
Likelihood using a quasi-discrete prior'', IEEE Int. Conf. on
Acoustics, Speech and Signal Processing (ICASSP), Jun. 2000.
[15] M.
Nikolova, ``Locally homogeneous images as minimizers of an objective function'', IEEE Int. Conf.
on Image Processing, Oct. 1999, vol.2, pp. 11-15, invited paper.
[14] M.
Nikolova, ``Local continuity and thresholding using truncated quadratic
regularization'', IEEE Workshop on Higher Order Statistics, pp. 277-280,
June 1999.
[13] M.
Nikolova and A. Hero III, ``Noisy word recognition using
denoising and moment matrix discriminants'', IEEE Workshop on Higher
Order Statistics, June 1999.
[12] F.
Alberge, P. Duhamel and M. Nikolova, ``Blind identification /
equalization using deterministic maximum likelihood and a partial information
on the input'', IEEE Workshop on Sig. Proc. Advances in Wireless Communications,
May 1999.
[11] M.
Nikolova, ``Estimation of binary images using convex criteria'', Proc. of IEEE
Int. Conf. on Image Processing (ICIP), Oct. 1998. (pdf)
[10] M.
Nikolova and A. Hero III, ``Segmentation of road edges from a vehicle-mounted
imaging radar'', Proc. of IEEE Stat. Signal and Array Proc., Sept. 1998. (pdf)
[9] M.
Nikolova, ``Estimation of signals containing strongly homogeneous zones'', Proc. of IEEE
Stat. Signal and Array Proc., Sept. 1998.
[8] M.
Nikolova, ``Reconstruction of locally homogeneous images'', European Signal
Proc. Conf., Sept. 1998.
[7] M.
Nikolova, ``Regularisation functions and estimators'', Proc. of IEEE Int. Conf. on
Image Processing (ICIP), Nov. 1996, pp. 457-460.
[6] M.
Nikolova, ``Non convex regularization and the recovery of edges'', Proc. IEEE
Workshop on Nonlinear Signal and Image
[5] M.
Nikolova, ``Parameter selection for a Markovian signal reconstruction with edge
detection'', Proc. IEEE Int. Conf. Acoust. Speech Signal Process.,
[4] M.
Nikolova, ``Markovian reconstruction in computed imaging and Fourier synthesis'', IEEE Int. Conf.
on Image Processing (ICIP), Nov. 1994, pp. 690-694.
[3] M.
Nikolova and A. Mohammad-Djafari, ``Discontinuity reconstruction
from linear attenuating operators using the weak-string model'', European Signal
Proc. Conf. (EUSIPCO), Sept. 1994, pp. 1062-1065. (pdf)
[2] M.
Nikolova, A. Mohammad-Djafari and J. Idier, ``Inversion of large-support
ill-conditionned linear operators using a Markov model with a line process'', Proc. IEEE Int. .
Acoust. Speech Signal Process. (ICASSP),
[1] M.
Nikolova and A. Mohammad-Djafari, ``Maximum entropy
image reconstruction in eddy current
tomography'', pp. 273–278, in Proc. of the 12th Int.
MaxEntWorkshop, Maximum Entropy and
Bayesian
Methods, 1992.