[1]
Auger, F. and Flandrin, P., Improving the readability of time-frequency and time-scale representations by the reassignment method, IEEE T. Signal Proces., 43 (1995), pp. 1068–1089.

[2]
Boashash, B., Estimating and interpreting the instantaneous frequency of a signal. I. fundamentals, P. IEEE, 80 (1992), pp. 520–538.

[3]
Cicone, A., Liu, J. and Zhou, H., Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis, Appl. Comput. Harmon. A., 41 (2016), pp. 384–411, http://dx.doi.org/10.1016/j.acha.2016.03.001.
[4]
Cicone, A., Liu, J. and Zhou, H., Hyperspectral chemical plume detection algorithms based on multidimensional iterative filtering decomposition, Phil. Trans. R. Soc. A, 374 (2016), doi: 10.1098/rsta.2015.0196.

[5]
Clausel, M., Oberlin, T. and Perrier, V., The monogenic synchrosqueezed wavelet transform: a tool for the decomposition/demodulation of am–fmimages, Appl. Comput. Harmon. A., 39 (2015), pp. 450–486.

[6]
Cohen, L., Time-Frequency Analysis, vol. 1406, Prentice Hall PTR Englewood Cliffs, NJ:, 1995.

[7]
Daubechies, I., Lu, J. and Wu, H. T., Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool, Appl. Comput. Harmon. A., 30 (2011), pp. 243–261.

[8]
Daubechies, I., Wang, Y. G. and Wu, H. T., Conceft: concentration of frequency and time via a multitapered synchrosqueezed transform, Phil. Trans. R. Soc. A, 374 (2016), pp. 20150193.

[9]
Dragomiretskiy, K. and Zosso, D., Variational mode decomposition, IEEE T. Signal. Proces., 62 (2014), pp. 531–544.

[10]
Gilles, J., Empirical wavelet transform, IEEE T. Signal. Proces., 61 (2013), pp. 3999–4010.

[11]
Gilles, J., Tran, G. and Osher, S., 2D empirical transforms, wavelets, ridgelets, and curvelets revisited, SIAM J. Imaging Sci., 7 (2014), pp. 157–186.

[12]
Havlicek, J. P., Havlicek, J. W., Mamuya, N. D. and Bovik, A. C., Skewed 2D Hilbert transforms and computed am-fm models, in Image Processing, 1998. ICIP 98. Proceedings., vol. 1, IEEE, 1998, pp. 602–606.

[13]
Hou, T. Y. and Shi, Z., Adaptive data analysis via sparse time-frequency representation, Adv. Adapt. Data Anal., 3 (2011), pp. 1–28.

[14]
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H. H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. Royal Soc. London. Ser. A, 454 (1998), pp. 903–995.

[15]
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G. and Woollen, J., et al., The ncep/ncar 40–year reanalysis project, Bulletin of the American meteorological Society, 77 (1996), pp. 437–471.

[16]
Lin, C. Y., Li, S. and Wu, H. T., *Wave-shape function analysis–when cepstrum meets time-frequency analysis*, preprint, (2016).

[17]
Lin, L., Wang, Y., and Zhou, H., Iterative filtering as an alternative algorithm for empirical mode decomposition, Adv. Adapt. Data Anal., 1 (2009), pp. 543–560.

[18]
Lorenzo-Ginori, J. V., An approach to the 2d Hilbert transform for image processing applications, in International Conference Image Analysis and Recognition, Springer, 2007, pp. 157–165.

[19]
Manolakis, D. and Shaw, G., Detection algorithms for hyperspectral imaging applications, IEEE Signal Proc. Mag., 19 (2002), pp. 29–43.

[20]
Pustelnik, N., Borgnat, P. and Flandrin, P., *A multicomponent proximal algorithm for empirical mode decomposition*, in Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, IEEE, 2012, pp. 1880–1884.

[21]
Schmitt, J., Pustelnik, N., Borgnat, P., and Flandrin, P., *2D Hilbert-huang transform*, in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2014, pp. 5377–5381.

[22]
Selesnick, I. W., Resonance-based signal decomposition: A new sparsity-enabled signal analysis method, Sig. Proc., 91 (2011), pp. 2793–2809.

[23]
Takeda, H., Farsiu, S. and Milanfar, P., Kernel regression for image processing and reconstruction, IEEE T. Image. Process., 16 (2007), pp. 349–366.

[24]
Wei, D. and Bovik, A., On the instantaneous frequencies of multicomponent am-fm signals, IEEE Signal Proc. Let., 5 (1998), pp. 84–86.

[25]
Wu, H. T., Flandrin, P. and Daubechies, I., One or two frequencies? the synchrosqueezing answers, Adv. Adap. Data An., 3 (2011), pp. 29–39.

[26]
Wu, Z. and Huang, N. E., Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adap. Data An., 1 (2009), pp. 1–41.

[27]
Wu, Z., Huang, N. E. and Chen, X., The multi–dimensional ensemble empirical mode decomposition method, Adv. Adap. Data An., 1 (2009), pp. 339–372.

[28]
Yang, H. and Ying, L., Synchrosqueezed wave packet transform for two–dimensional mode decomposition, SIAM J. Imaging Sci., 6 (2013), pp. 1979–2009.

[29]
Yang, H. and Ying, L., Synchrosqueezed curvelet transform for two–dimensional mode decomposition, SIAM J. Math. Anal., 46 (2014), pp. 2052–2083.