World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
42
Citations
9043
World Ranking
8275
National Ranking
503

Research.com Recognitions

  • 2017 - IEEE Fellow For contributions to sparse signal representation and sampling theory

Overview

Pier Luigi Dragotti is affiliated with Imperial College London in the United Kingdom. Their research spans multiple fields with primary focus areas including Computer Science and Engineering.

Their work addresses key subfields such as Computer Vision and Pattern Recognition, Biophysics, Electrical and Electronic Engineering, Artificial Intelligence, and Radiology, Nuclear Medicine, and Imaging.

Frequent topics in their research include:

  • Image and Signal Denoising Methods
  • Advanced Fluorescence Microscopy Techniques
  • Advanced Image Processing Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Memory and Neural Computing
  • Neural Dynamics and Brain Function
  • Optical Coherence Tomography Applications

Their publication record includes numerous contributions to venues such as:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Transactions on Computational Imaging
  • 2021 29th European Signal Processing Conference (EUSIPCO)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Among recent papers authored in collaboration with others are:

  • Neural heterogeneity promotes robust learning, 2021, Nature Communications
  • Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • WINNet: Wavelet-Inspired Invertible Network for Image Denoising, 2022, IEEE Transactions on Image Processing
  • AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis, 2022, Proceedings of the IEEE
  • Generative Joint Source-Channel Coding for Semantic Image Transmission, 2023, IEEE Journal on Selected Areas in Communications

Frequent co-authors with whom they have published extensively include:

  • Junjie Huang
  • Herman Verinaz-Jadan
  • Carmel L. Howe
  • Pingfan Song
  • Amanda J. Foust

They have been recognized as an IEEE Fellow in 2017 for contributions to sparse signal representation and sampling theory.

Best Publications

  • DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction

    Guang Yang;Simiao Yu;Hao Dong;Greg Slabaugh

  • Sampling Moments and Reconstructing Signals of Finite Rate of Innovation: Shannon Meets Strang–Fix

    P.L. Dragotti;M. Vetterli;T. Blu

  • Sparse Sampling of Signal Innovations

    T. Blu;P.-L. Dragotti;M. Vetterli;P. Marziliano

  • Compression of multispectral images by three-dimensional SPIHT algorithm

    P. Luigi Dragotti;G. Poggi;A.R.P. Ragozini

  • Directionlets: anisotropic multidirectional representation with separable filtering

    V. Velisavljevic;B. Beferull-Lozano;M. Vetterli;P.L. Dragotti

  • The Distributed Karhunen–Loève Transform

    M. Gastpar;P.L. Dragotti;M. Vetterli

  • Neural heterogeneity promotes robust learning.

    Nicolas Perez-Nieves;Vincent C. H. Leung;Pier Luigi Dragotti;Dan F. M. Goodman

  • Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion

    Xin Deng;Pier Luigi Dragotti

  • Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images

    R. Shukla;P.L. Dragotti;M.N. Do;M. Vetterli

  • Wavelet footprints: theory, algorithms, and applications

    P.L. Dragotti;M. Vetterli

  • Filter bank frame expansions with erasures

    J. Kovacevic;P.L. Dragotti;V.K. Goyal

  • AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis

    Unknown

  • Distributed Source Coding: Theory, Algorithms and Applications

    Pier Luigi Dragotti;Michael Gastpar

  • Generative Joint Source-Channel Coding for Semantic Image Transmission

    Unknown

  • FRI Sampling With Arbitrary Kernels

    Jose Antonio Uriguen;Thierry Blu;Pier Luigi Dragotti

  • Exact Feature Extraction Using Finite Rate of Innovation Principles With an Application to Image Super-Resolution

    L. Baboulaz;P.L. Dragotti

  • A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging

    Jon Oñativia;Simon R Schultz;Pier Luigi Dragotti

  • WINNet: Wavelet-inspired Invertible Network for Image Denoising

    Jun-Jie Huang;Pier Luigi Dragotti

  • Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries

    Pingfan Song;Xin Deng;Joao F. C. Mota;Nikos Deligiannis

  • Sensing reality and communicating bits: a dangerous liaison

    M. Gastpar;M. Vetterli;P.L. Dragotti

  • Sampling Schemes for Multidimensional Signals With Finite Rate of Innovation

    P. Shukla;P.L. Dragotti

  • Sampling Piecewise Sinusoidal Signals With Finite Rate of Innovation Methods

    J. Berent;P.L. Dragotti;T. Blu

  • Symmetric and asymmetric Slepian-Wolf codes with systematic and nonsystematic linear codes

    N. Gehrig;P.L. Dragotti

  • Rate-Distortion Optimized Tree-Structured Compression Algorithms for Piecewise

    Rahul Shukla;Pier Luigi Dragotti;Minh N. Do;Martin Vetterli

Frequent Co-Authors

Martin Vetterli
Martin Vetterli École Polytechnique Fédérale de Lausanne
Yue M. Lu
Yue M. Lu Beijing University of Posts and Telecommunications
Deniz Gunduz
Deniz Gunduz Imperial College London
Thierry Blu
Thierry Blu Chinese University of Hong Kong
Michael Gastpar
Michael Gastpar École Polytechnique Fédérale de Lausanne
Marco Tagliasacchi
Marco Tagliasacchi Google (Switzerland)
Minh N. Do
Minh N. Do University of Illinois at Urbana-Champaign
Stefano Tubaro
Stefano Tubaro Polytechnic University of Milan
Mai Xu
Mai Xu Beihang University
Simon R. Arridge
Simon R. Arridge University College London

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