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Articles
[50] D.-C. Soncco, C. Barbanson, M. Nikolova, A. Almansa, and Y. Ferrec, “Fast and Accurate Multiplicative Decomposition for Fringe Removal in
Interferometric Images”, IEEE Trans. Computational Imaging, Jun., 2017, vol. 3, issue 2, pp. 187
– 201, doi 10.1109/TCI.2017.2678279 (pdf)
[49] X. Cai, R. Chan, M. Nikolova, and T. Zeng, “A Three-stage Approach for
Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)”, Journal of Scientific Computing,
2017, doi 10.1007/s10915-017-0402-2 (pdf)
[48] F. Laus, M. Nikolova, J. Persch, and G. Steidl, “A
nonlocal denoising algorithm for manifold-valued
images using second order statistics”, SIAM Journal on Imaging Science,
vol. 10, issue 1, (2017), pp. 416–448
[47] J.-F. Aujol, M. Nikolova, and N. Papadakis, “Guest Editorial: Scale-Space and Variational Methods”, J Math Imaging Vis (2016) 56:173–174.
[46]
M. Nikolova, "Relationship between the optimal solutions of
least squares regularized with L0-norm and constrained by k-sparsity", Appl. Comput. Harmon. Anal., vol. 41, issue 1, July 2016, pp. 237 - 265 (pdf)
[45] X. Cai, J.-H. Fitschen,
M. Nikolova, G. Steidl and
M. Storath, "Disparity and Optical Flow Partitioning Using Extended Potts Priors", Information and Inference : A
Journal of the IMA, vol
4, issue 1, March 2015, pp. 43-62 (pdf)
[44]
R. Chan, H-X. Liang, S.Wei, M. Nikolova and X-C. Tai, "High-order Total Variation
Regularization Approach for Axially Symmetric Object Tomography from a Single
Radiograph", Inverse Problems and Imaging, vol.
9, n. 1, 2015 (pdf)
[43] M. Nikolova
and G. Steidl, "Fast ordering algorithm for exact histogram
specification", IEEE Trans. on Image Processing, Dec. 2014, vol. 23, n. 12, pp.
5274-5283 (pdf)
[42] M. Nikolova
and G. Steidl, "Fast Hue and Range Preserving Histogram Specification:
Theory and New Algorithms for Color Image", IEEE
Trans. on Image Processing, Sep. 2014, vol. 23, n. 9, pp. 4087-4100
(pdf)
[40] F. Bauss,
M. Nikolova and G. Steidl, " Fully smoothed ℓ 1- TV models: Bounds for the
minimizers and parameter choice ", Journal of
Mathematical Imaging and Vision, online Feb 2013 (pdf)
[39]
M. Nikolova, Y-W. Wen and R. Chan, "Exact Histogram Specifcation for Digital Images
Using a Variational Approach", online November 2012, Journal of Mathematical
Imaging and Vision, 2013, vol. 46, n. 3, pp. 309-325 (pdf)
[38] M. Nikolova,
M. Ng and C. P. Tam, "On ℓ1 Data Fitting and Concave Regularization
for Image Recovery", SIAM J. on Scientific Computing, vol. 35, n. 1, pp. A397-A430,
online Jan 2013 (pdf).
[37] M. Nikolova, "Solve exactly an underdetermined linear system by minimizing least squares with an ℓ0 penalty",
Comptes-rendus de l’Académie des sciences, Série I (Mathématiques) 349, Nov. 2011, pp. 1145-1150 (pdf)
[36] F. Malgouyres
and M. Nikolova, "Average performance of the sparsest approximation using a general
dictionary", Numerical Functional Analysis and Optimization (NFAO), 32(7), pp. 768-805,
2011 (pdf)
[35] A. Antoniadis, I. Gijbels and M. Nikolova, "Penalized Likelihood Regression for Generalized Linear Models with Nonquadratic Penalties ", Annals of the Instutute of Statistical Mathematics, June 2011, vol. 63,
n. 3, pp. 585-615 (pdf).
[34]
M. Nikolova, M. Ng and C. P. Tam, "A Fast Nonconvex Nonsmooth Minimization Method
for Image Restoration and Reconstruction", IEEE
Trans. on ImageProcessing, Vol. 19, .n 12, Dec. 2010 (pdf).
[33] S. Durand S., J. Fadili and M. Nikolova, "Multiplicative noise removal using L1 fidelity on frame coefficients", Journal
of Mathematical Imaging and Vision,
(Online 2009), Mar. 2010, vol. 36, n. 3, pp. 201-226 (pdf).
[32]
Cai J.-F., R. Chan and M. Nikolova. "Fast Two-Phase Image Deblurring under Impulse Noise ", Journal of Mathematical Imaging and Vision, (Online 2009), Jan. 2010, vol. 36, n. 1, pp. 46-53 (pdf).
[31] M. Nikolova, "One-iteration dejittering of digital video
images", Journal of Visual Communication and
Image Representation, Vol. 20, 2009, pp. 254-274 (pdf).
[30]
M. Nikolova and F. Malgouyres. "Average
performance of the approximation in a dictionary using an
ℓ0 objective", Comptes-rendus de l'Académie des sciences, Série I (Mathématiques)
347, 2009, pp. 565-570. (pdf)
[29] M. Nikolova. "Semi-explicit
solution and fast minimization scheme for an energy with L1-fitting and
Tikhonov-like regularization ", Journal of Mathematical Imaging and Vision, Vol. 34, № 1, 2009, pp. 32-47 (pdf)
[28] Cai J-F., R. Chan
and M. Nikolova, “Two phase methods for deblurring images
corrupted by impulse plus Gaussian noise ", AIMS Journal on
Inverse Problems and Imaging, Vol. 2, n. 2, April 2008, pp. 187-204. (pdf)
[27] Nikolova M., M. Ng, S. Zhang
and W-K. Ching, "Efficient reconstruction of piecewise
constant images using nonsmooth nonconvex
minimization", SIAM Journal on Imaging Sciences, vol. 1, n. 1, Mar.
2008, pp. 2-25. (pdf)
[26] M. Nikolova,
''Analytical bounds on the minimizers of
(nonconvex) regularized least-squares'', AIMS Journal on Inverse
Problems and Imaging, 2007, vol. 1, N.4, 2007, pp. 661-677 (pdf)
[25] Nikolova M., ''Model distortions in Bayesian MAP reconstruction'', AIMS Journal
on Inverse Problems and Imaging, vol. 1, N. 2, 2007, pp. 399-422 (pdf)
[24] Durand S. and M. Nikolova, "Denoising of frame coefficients using ℓ1
data-fidelity term and edge-preserving regularization", SIAM
Journal on Multiscale Modeling and Simulation,
vol. 6, n. 2, 2007, pp.547-576. (pdf)
[23] Nikolova M. and R. Chan,
"The equivalence of Half-Quadratic
Minimization and the Gradient Linearization Iteration'', IEEE Trans. on
Image Processing, June 2007, vol. 16, n. 6, pp. 1623-1627 (pdf).
[22] Chan Tony, Selim Esedoglu and Mila Nikolova, "Algorithms
for Finding Global Minimizers of Image Segmentation and Denoising
Models, SIAM J. on Applied Mathematics, vol. 66, n. 5, 2006,
pp.1632-1648. (pdf)
[21] Durand S. and Nikolova M. ``Stability
of the Minimizers of Least Squares with a Non-Convex Regularization. Part I:
Local Behavior'', Journal of Applied
Mathematics and Optimization, Vol. 53, n. 2, March 2006, pp. 185-208. (pdf)
[20] Durand S. and Nikolova M. ``Stability
of the Minimizers of Least Squares with a Non-Convex Regularization. Part II:
Global Behavior'', Journal of Applied
Mathematics and Optimization, Vol. 53, n. 3, May 2006, pp. 259-277. (pdf)
[19] Haoying Fu H., M. Ng, M. Nikolova and J. Barlow, "Efficient minimization methods of mixed ℓ1 - ℓ1
and ℓ2 - ℓ1 norms for image restoration", SIAM Journal on Scientific computing, Vol. 27, No 6, 2006, pp
1881-1902. (pdf)
[18] Alberge F., M. Nikolova and P. Duhamel, "Blind Identification / Equalization using Deterministic Maximum
Likelihood and a partial prior on the input'', IEEE Trans. on Signal
Processing, Vol. 54, Issue 2, Feb. 2006, pp. 724- 737. (pdf)
[17] Nikolova M. and M. Ng, "Analysis of Half-Quadratic Minimization Methods for Signal and Image
Recovery'', SIAM Journal on Scientific computing, vol. 27, No. 3, 2005,
pp. 937-966. (pdf)
[16] Chan R., Chung-Wa Ho and M. Nikolova,
"Salt-and-Pepper Noise Removal by
Median-type Noise Detector and Detail-Preserving Regularization", IEEE Trans.
on Image Processing, Vol. 14, No. 10, Oct. 2005, pp. 1479-1485. (pdf)
[15] Nikolova M., ''Analysis of the recovery of edges in images and signals by minimizing
nonconvex regularized least-squares'', SIAM Journal on
Multiscale Modeling and Simulation, vol. 4, N. 3,
2005, pp. 960-991 (pdf)
[14] Chan R., C. Hu and M. Nikolova, "An
Iterative Procedure for Removing Random-Valued Impulse Noise", IEEE Signal Processing Letters, 11
(2004), 921-924. (pdf)
[13] Chan R., C.W. Ho and M. Nikolova, "Convergence of Newton's Method for a Minimization Problem in Impulse
Noise Removal'', J. Comput. Math., vol. 22, 2004,
pp. 168-177. (pdf)
[12] R. Peeters,
P. Kornprobst, M. Nikolova,
S. Sunaert, T. Vieville, G.
Malandain, R. Deriche, O. Fougeras,
M. Ng and P. Hecke, "The use of superresolution techniques to reduce slice thickness in
functional MRI'', International Journal of Imaging Systems and Technology, Vol.
14, No. 3, 2004. (pdf), DOI : 10.1002/ima.20016
[11]
Nikolova M., ''A variational approach to remove outliers and
impulse noise'', Journal of Mathematical Imaging and Vision, vol. 20, no. 1-2,
2004, pp. 99-120. (pdf)
[10] Nikolova M., ''Weakly constrained minimization. Application to the estimation of images
and signals involving constant regions'', Journal of Mathematical
Imaging and Vision, no. 2, vol. 21, Sep.
2004, pp. 155-175. (pdf)
[9] Roullot E., A. Herment, I. Bloch, A. Cesare, M. Nikolova
and E. Mousseaux, "Modeling anisotropic undersampling of magnetic resonance angiographies and
reconstruction of a high-resolution isotropic volume using half-quadratic
regularization techniques'', Signal Processing, vol. 84, 2004, pp.
743-762. (pdf)
[8] Nikolova M., ''Minimizers of cost-functions involving non-smooth data-fidelity terms. Application to the processing of outliers'', SIAM Journal on Numerical
Analysis vol. 40, no. 3, 2002, pp. 965-994. (pdf)
[7] Alberge F., P. Duhamel and M.
Nikolova, "Adaptive
solution for blind identification / equalization using deterministic maximum
likelihood'', IEEE Trans. on Signal Processing, vol. 50, no 4, April
2002, pp. 923-936. (pdf)
[6] Nikolova M., ''Local strong homogeneity of a regularized estimator'', SIAM Journal on
Applied Mathematics, vol. 61, no. 2, pp. 633-658, 2000. (pdf)
[5] Nikolova M., ''Thresholding implied by truncated quadratic regularization'', IEEE Trans. on
Signal Processing, vol. 48, Dec. 2000, pp. 3437-3450.(pdf)
[4] Nikolova M., "Markovian reconstruction using a GNC approach'', IEEE Trans. on
Image Processing , vol. 8, no. 9, Sept. 1999, pp. 1204-1220. (pdf)
[3] Nikolova M., Idier J. and Mohammad-Djafari A.,
"Inversion of large-support ill-posed
linear operators using a piecewise Gaussian MRF'', IEEE Trans. On Image Processing, vol. 8, no. 4, pp. 571-585, April 1998. (pdf)
[2] Nikolova M., ''Estimées localement fortement homogènes = Locally
strongly homogeneous estimates'', Comptes-rendus de l'Académie des sciences, Série
I (Mathématiques), Paris, vol. 325, n. 6, p. 665-670, 1997. (pdf)
[1] Nikolova M. and A. Mohammad-Djafari, "Eddy Current Tomography Using a
Markov model'', Signal Processing, vol. 49, no. 2, 1996. (ps)