Imperial College London

ProfessorPier LuigiDragotti

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Professor of Signal Processing



+44 (0)20 7594 6192p.dragotti




814Electrical EngineeringSouth Kensington Campus






I am Professor of Signal Processing in the Electrical and Electronic Engineering Department at Imperial College London. I am interested in mathematical methods for signal processing and high-dimensional data analysis. I apply these methods to develop interpretable model-based neural networks for computational imaging. The following articles and presentations highlight my recent research activities:

For more information about my research activities, project, PhD students, talks, software and teaching, please view my personal web page at


Selected Publications

Journal Articles

Huang J-J, Dragotti PL, 2022, WINNet: wavelet-inspired invertible network for image denoising, IEEE Transactions on Image Processing, Vol:31, ISSN:1057-7149, Pages:4377-4392

Deng X, Dragotti PL, 2020, Deep convolutional neural network for multi-modal image restoration and fusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol:43, ISSN:0162-8828, Pages:3333-3348

Alexandru R, Dragotti PL, 2020, Reconstructing classes of non-bandlimited signals from time encoded information, IEEE Transactions on Signal Processing, Vol:68, ISSN:1053-587X, Pages:747-763

Deng X, Dragotti PL, 2019, Deep coupled ISTA network for multi-modal image super-resolution, IEEE Transactions on Image Processing, Vol:29, ISSN:1057-7149, Pages:1683-1698

Dragotti P, Murray-Bruce J, 2017, A Sampling Framework for Solving Physics-driven Inverse Source Problems, IEEE Transactions on Signal Processing, Vol:65, ISSN:1053-587X, Pages:6365-6380

Dragotti P, Wei X, 2016, FRESH – FRI-based single-image super-resolution algorithm, IEEE Transactions on Image Processing, Vol:25, ISSN:1057-7149, Pages:3723-3735

Pan H, Blu T, Dragotti PL, 2014, Sampling curves with finite rate of innovation, IEEE Transactions on Signal Processing, Vol:62, ISSN:1053-587X, Pages:458-471

Urigueen JA, Blu T, Dragotti PL, 2013, FRI Sampling With Arbitrary Kernels, IEEE Transactions on Signal Processing, Vol:61, ISSN:1053-587X, Pages:5310-5323

Pearson J, Brookes M, Dragotti P-L, 2013, Plenoptic layer-based modelling for image based rendering, IEEE Transactions on Image Processing, Vol:22, ISSN:1057-7149, Pages:3405-3419

Oñativia J, Schultz S, Dragotti PL, 2013, A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging, Journal of Neural Engineering, Vol:10

Gelman A, Dragotti PL, Velisavlievic V, 2012, Multi-view Image Coding using Depth Layersand an Optimized Bit Allocation, Ieee Transactions on Image Processing

Chaisinthop V, Dragotti PL, 2011, Centralized and Distributed Semiparametric Compression of Piecewise Smooth Functions, IEEE Transactions on Signal Processing, Vol:59, ISSN:1053-587X, Pages:3071-3085

Berent J, Dragotti PL, Blu T, 2010, Sampling Piecewise Sinusoidal Signals With Finite Rate of Innovation Methods, IEEE Transactions on Signal Processing, Vol:58, ISSN:1053-587X, Pages:613-625

Baboulaz L, Dragotti PL, 2009, Exact Feature Extraction Using Finite Rate of Innovation Principles With an Application to Image Super-Resolution, IEEE Transactions on Image Processing, Vol:18, ISSN:1057-7149, Pages:281-298

Blu T, Dragotti P-L, Vetterli M, et al., 2008, Sparse sampling of signal innovations, IEEE Signal Processing Magazine, Vol:25, ISSN:1053-5888, Pages:31-40

Dragotti, P L, Vetterli, et al., 2007, Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets Strang–Fix, IEEE Transactions on Signal Processing, Vol:55, ISSN:1053-587X, Pages:1741-1757

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