A Wavelet Tour of Signal Processing

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Compressed sensing

Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898

Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880

An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.

Daubechies, I., Defrise, M., & De Mol, C. (2004) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20042 · Cited by 4,958

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014) · arXiv · doi:10.48550/arxiv.1412.7062 · Cited by 4,213

Geometric Deep Learning: Going beyond Euclidean data

Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661

Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

Wang, Z., & Bovik, A. C. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.930649 · Cited by 3,175

Untitled work

(1998) · Cited by 2,679

A sparse signal reconstruction perspective for source localization with sensor arrays

Malioutov, D., Cetin, M., & Willsky, A. S. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.850882 · Cited by 2,638

Deep Convolutional Neural Network for Inverse Problems in Imaging

Jin, K. H., McCann, M. T., Froustey, E., & Unser, M. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2713099 · Cited by 2,529

Bayesian compressive sensing

Ji, S., Xue, Y., & Carin, L. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2007.914345 · Cited by 2,406

Time–frequency dynamics of resting-state brain connectivity measured with fMRI

Chang, C., & Glover, G. H. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.011 · Cited by 1,808

NICE: Non-linear Independent Components Estimation

Dinh, L., Krueger, D., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1410.8516 · Cited by 1,493

Battery Lifetime Prognostics

Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320

Deep Convolutional Networks on Graph-Structured Data

Henaff, M., Bruna, J., & LeCun, Y. (2015) · arXiv · doi:10.48550/arxiv.1506.05163 · Cited by 1,255

Fast Image Recovery Using Variable Splitting and Constrained Optimization

Afonso, M. V., Bioucas-Dias, J. M., & Figueiredo, M. A. T. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2047910 · Cited by 1,223

Fully Convolutional Instance-Aware Semantic Segmentation

Li, Y., Qi, H., Dai, J., Ji, X., & Wei, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.472 · Cited by 1,116

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

Tropp, J. A., Laska, J. N., Duarte, M. F., Romberg, J. K., & Baraniuk, R. G. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2034811 · Cited by 1,089

Wavelet transforms and the ECG: a review

Addison, P. S. (2005) · Physiological Measurement · doi:10.1088/0967-3334/26/5/r01 · Cited by 908

Sparse geometric image representations with bandelets

Le Pennec, E., & Mallat, S. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.843753 · Cited by 860

Biohashing: two factor authentication featuring fingerprint data and tokenised random number

Jin, A. T. B., Ling, D. N. C., & Goh, A. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.04.011 · Cited by 776

Wedgelets: nearly minimax estimation of edges

Donoho, D. L. (1999) · The Annals of Statistics · doi:10.1214/aos/1018031261 · Cited by 638

Preprocessing techniques for context recognition from accelerometer data

Figo, D., Diniz, P. C., Ferreira, D. R., & Cardoso, J. M. P. (2010) · Personal and Ubiquitous Computing · doi:10.1007/s00779-010-0293-9 · Cited by 578

Fault Detection in Induction Machines Using Power Spectral Density in Wavelet Decomposition

Cusid�Cusido, J., Romeral, L., Ortega, J. A., Rosero, J. A., & Garc�aGarcia Espinosa, A. (2008) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2007.911960 · Cited by 512

Object Detection Networks on Convolutional Feature Maps

Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2601099 · Cited by 454

Object Detection Networks on Convolutional Feature Maps

Ren, S., He, K., Girshick, R., Zhang, X., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1504.06066 · Cited by 454

A Review Paper : Noise Models in Digital Image Processing

Boyat, A. K., & Joshi, B. K. (2015) · Signal & Image Processing : An International Journal · doi:10.5121/sipij.2015.6206 · Cited by 418

Acoustic sequences in non‐human animals: a tutorial review and prospectus

Kershenbaum, A., Blumstein, D. T., Roch, M. A., Akçay, Ç., Backus, G., Bee, M. A., Bohn, K., Cao, Y., Carter, G., Cäsar, C., et al. (2016) · Biological Reviews · doi:10.1111/brv.12160 · Cited by 337

Vertex-frequency analysis on graphs

Shuman, D. I., Ricaud, B., & Vandergheynst, P. (2016) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2015.02.005 · Cited by 349

Automatic Defect Detection on Hot-Rolled Flat Steel Products

Ghorai, S., Mukherjee, A., Gangadaran, M., & Dutta, P. K. (2013) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2012.2218677 · Cited by 317

Theta EEG dynamics of the error-related negativity

Trujillo, L. T., & Allen, J. J. B. (2007) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.11.009 · Cited by 314

Reduced- and No-Reference Image Quality Assessment

Wang, Z., & Bovik, A. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.942471 · Cited by 280

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

Hong, Y., Pan, H., Sun, W., & Jia, Y. (2021) · arXiv · doi:10.48550/arxiv.2101.06085 · Cited by 261

Wheel Defect Detection With Machine Learning

Krummenacher, G., Ong, C. S., Koller, S., Kobayashi, S., & Buhmann, J. M. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2720721 · Cited by 258

A Fast Wavelet-Based Reconstruction Method for Magnetic Resonance Imaging

Guerquin-Kern, M., Haberlin, M., Pruessmann, K. P., & Unser, M. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2140121 · Cited by 157

Discrimination of pathological voices using a time-frequency approach

Umapathy, K., Krishnan, S., Parsa, V., & Jamieson, D. G. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.842962 · Cited by 149

Gaussian process models for mitigation of operational variability in the structural health monitoring of wind turbines

Avendaño-Valencia, L. D., Chatzi, E. N., & Tcherniak, D. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106686 · Cited by 110

A robust methodology for classification of epileptic seizures in EEG signals

Tzimourta, K. D., Tzallas, A. T., Giannakeas, N., Astrakas, L. G., Tsalikakis, D. G., Angelidis, P., & Tsipouras, M. G. (2019) · Health and Technology · doi:10.1007/s12553-018-0265-z · Cited by 109

Spatial statistics, spatial regression, and graph theory in ecology

Fortin, M.-J., James, P. M. A., MacKenzie, A., Melles, S. J., & Rayfield, B. (2012) · Spatial Statistics · doi:10.1016/j.spasta.2012.02.004 · Cited by 107

The tropical Atlantic meridional SST gradient index and its relationships with the SOI, NAO and Southern Ocean

Mélice, J.-L., & Servain, J. (2003) · Climate Dynamics · doi:10.1007/s00382-002-0289-x · Cited by 95

Comprehensive review on inter‐turn fault indexes in permanent magnet motors

Faiz, J., Nejadi‐Koti, H., & Valipour, Z. (2017) · IET Electric Power Applications · doi:10.1049/iet-epa.2016.0196 · Cited by 86

STC Antispoofing Systems for the ASVspoof2021 Challenge

Tomilov, A., Svishchev, A., Volkova, M., Chirkovskiy, A., Kondratev, A., & Lavrentyeva, G. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-10 · Cited by 71

Multiscale Analysis of Intensive Longitudinal Biomedical Signals and Its Clinical Applications

Nakamura, T., Kiyono, K., Wendt, H., Abry, P., & Yamamoto, Y. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2491979 · Cited by 64

On the short-circuiting Fault Detection in a PMSM by means of Stator Current Transformations

Rosero, J. A., Romeral, L., Cusido, J., Garcia, A., & Ortega, J. A. (2007) · 2007 IEEE Power Electronics Specialists Conference · doi:10.1109/pesc.2007.4342300 · Cited by 63

Application of Shearlet transform to classification of surface defects for metals

Xu, K., Liu, S., & Ai, Y. (2015) · Image and Vision Computing · doi:10.1016/j.imavis.2015.01.001 · Cited by 55

Application of Undecimated Wavelet Transform to Surface Defect Detection of Hot Rolled Steel Plates

Wu, X.-Y., Xu, K., & Xu, J.-W. (2008) · 2008 Congress on Image and Signal Processing · doi:10.1109/cisp.2008.278 · Cited by 53

WSPM: Wavelet-based statistical parametric mapping

Van De Ville, D., Seghier, M. L., Lazeyras, F., Blu, T., & Unser, M. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.06.011 · Cited by 46

Fault detection of eccentricity and bearing damage in a PMSM by means of wavelet transforms decomposition of the stator current

Rosero, J., Romeral, J. L., Cusido, J., Ortega, J. A., & Garcia, A. (2008) · 2008 Twenty-Third Annual IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec.2008.4522708 · Cited by 44

State- or trait-like individual differences in dream recall: preliminary findings from a within-subjects study of multiple nap REM sleep awakenings

Scarpelli, S., Marzano, C., D’Atri, A., Gorgoni, M., Ferrara, M., & De Gennaro, L. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00928 · Cited by 38

Differential Associations of Early- and Late-Night Sleep with Functional Brain States Promoting Insight to Abstract Task Regularity

Yordanova, J., Kolev, V., Wagner, U., & Verleger, R. (2010) · PLoS ONE · doi:10.1371/journal.pone.0009442 · Cited by 31

Diffuse terahertz spectroscopy in turbid media using a wavelet-based bimodality spectral analysis

Khani, M. E., Osman, O. B., & Arbab, M. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-02068-7 · Cited by 28

Effect of medication in Parkinson’s disease: a wavelet analysis of EMG signals

Strambi, S.-K., Rossi, B., De Michele, G., & Sello, S. (2004) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2004.01.006 · Cited by 22

Fault Detection of PMSM under Non-Stationary Conditions Based on Wavelet Transformation Combined with Distance Approach

Park, C. H., Lee, J., Ahn, G., Youn, M., & Youn, B. D. (2019) · 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) · doi:10.1109/demped.2019.8864842 · Cited by 21

SDP Approximation of a Fractional Delay and the Design of Dual-Tree Complex Wavelet Transform

Dumitrescu, B. (2008) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2008.924134 · Cited by 15

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence

Azadeh, S., Hobbs, B. P., Ma, L., Nielsen, D. A., Gerard Moeller, F., & Baladandayuthapani, V. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.033 · Cited by 12

Periodicity, Elliott waves, and fractals in the NFT market

Christopher Westland, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-55011-x · Cited by 10

Efficient levels of spatial pyramid representation for local binary patterns

Akoushideh, A., & Mazloom‐Nezhad Maybodi, B. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0028 · Cited by 7

Fault diagnosis of single point cutting tool using spectrum, cepstrum and wavelet analysis

Aralikatti, S. S., Ravikumar, K. N., & Kumar, H. (2019) · AIP Conference Proceedings · doi:10.1063/1.5141218 · Cited by 3

Terahertz time domain spectroscopy data processing: analysing uncertainties to push boundaries

Lavancier, M., Eliet Barois, S., Denakpo, E., Vlieghe, J., Vindas, N., Hindle, F., Cuisset, A., & Peretti, R. (2022) · Terahertz Photonics II · doi:10.1117/12.2620069 · Cited by 2