World's Best Scientists 2026 revealed!

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Computer Science

D-Index
65
Citations
23983
World Ranking
2407
National Ranking
1202

Electronics and Electrical Engineering

D-Index
64
Citations
23474
World Ranking
1263
National Ranking
522

Research.com Recognitions

  • 2016 - IEEE Fellow For contributions to wavelet and sparsity based signal processing

Overview

Ivan W. Selesnick is affiliated with New York University in the United States. Their research spans several fields including Engineering, Computer Science, and Medicine, with a focus on subfields such as Computer Vision and Pattern Recognition, Computational Mechanics, Biomedical Engineering, Control and Systems Engineering, and Civil and Structural Engineering.

The topics frequently addressed in their work include Image and Signal Denoising Methods, Sparse and Compressive Sensing Techniques, Photoacoustic and Ultrasonic Imaging, Structural Health Monitoring Techniques, Machine Fault Diagnosis Techniques, Advanced Image Fusion Techniques, and Glaucoma and retinal disorders.

Among recent publications authored or coauthored by Ivan W. Selesnick are:

  • Non-convex Total Variation Regularization for Convex Denoising of Signals, 2020, Journal of Mathematical Imaging and Vision
  • Reweighted generalized minimax-concave sparse regularization and application in machinery fault diagnosis, 2020, ISA Transactions
  • Discriminative Dictionary Learning-Based Sparse Classification Framework for Data-Driven Machinery Fault Diagnosis, 2021, IEEE Sensors Journal
  • Ridge-Aware Weighted Sparse Time-Frequency Representation, 2020, IEEE Transactions on Signal Processing
  • Fault diagnosis for rolling bearings under unknown time-varying speed conditions with sparse representation, 2020, Journal of Sound and Vibration

Ivan W. Selesnick frequently collaborates with researchers including Abdullah Al-Shabili, John-Ross Rizzo, Todd E. Hudson, Janet C. Rucker, and Weiwei Dai.

Their research has been published in venues such as:

  • IEEE Transactions on Signal Processing
  • arXiv (Cornell University)
  • Pattern Recognition Letters
  • IEEE Transactions on Image Processing
  • Journal of Mathematical Imaging and Vision

Ivan W. Selesnick was recognized as an IEEE Fellow in 2016 for contributions to wavelet and sparsity-based signal processing.

Best Publications

  • Introduction to Wavelets and Wavelet Transforms: A Primer

    C. S. Burrus;Ramesh A. Gopinath;Haitao Guo;Jan E. Odegard

  • The dual-tree complex wavelet transform

    I.W. Selesnick;R.G. Baraniuk;N.C. Kingsbury

  • Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency

    L. Sendur;I.W. Selesnick

  • Bivariate shrinkage with local variance estimation

    L. Sendur;I.W. Selesnick

  • Wavelet Transform With Tunable Q-Factor

    I. W. Selesnick

  • Hilbert transform pairs of wavelet bases

    I.W. Selesnick

  • Generalized digital Butterworth filter design

    I.W. Selesnick;C.S. Burrus

  • The double-density dual-tree DWT

    I.W. Selesnick

  • Sparse Regularization via Convex Analysis

    Ivan Selesnick

  • The design of approximate Hilbert transform pairs of wavelet bases

    I.W. Selesnick

  • Wavelet based speckle reduction with application to SAR based ATD/R

    H. Guo;J.E. Odegard;M. Lang;R.A. Gopinath

  • Full length article: Emerging applications of wavelets: A review

    Ali N. Akansu;Wouter A. Serdijn;Ivan W. Selesnick

  • Resonance-based signal decomposition: A new sparsity-enabled signal analysis method

    Ivan W. Selesnick

  • Video denoising using 2D and 3D dual-tree complex wavelet transforms

    Ivan W. Selesnick;Ke Yong Li

  • Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization

    Po-Yu Chen;Ivan W. Selesnick

  • Nonconvex Sparse Regularization and Convex Optimization for Bearing Fault Diagnosis

    Shibin Wang;Ivan Selesnick;Gaigai Cai;Yining Feng

  • Chromatogram baseline estimation and denoising using sparsity (BEADS)

    Xiaoran Ning;Ivan W. Selesnick;Laurent Duval;Laurent Duval

  • Constrained least square design of FIR filters without specified transition bands

    I.W. Selesnick;M. Lang;C.S. Burrus

  • Signal restoration with overcomplete wavelet transforms: comparison of analysis and synthesis priors

    Ivan W. Selesnick;Mário A. T. Figueiredo

  • The slantlet transform

    I.W. Selesnick

Frequent Co-Authors

C.S. Burrus
C.S. Burrus Rice University
Hossein Rabbani
Hossein Rabbani Isfahan University of Medical Sciences
Yao Wang
Yao Wang New York University
Ramesh A. Gopinath
Ramesh A. Gopinath IBM (United States)
Braham Himed
Braham Himed United States Air Force Research Laboratory
Saeed Gazor
Saeed Gazor Queen's University
Ivan Bodis-Wollner
Ivan Bodis-Wollner SUNY Downstate Medical Center
Anthony Vetro
Anthony Vetro Mitsubishi Electric (United States)
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Vahe E. Amassian
Vahe E. Amassian SUNY Downstate Medical Center

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