What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Artificial intelligence
- Statistics
Thierry Blu spends much of his time researching Algorithm, Wavelet, Mathematical optimization, Wavelet transform and Spline.
The concepts of his Algorithm study are interwoven with issues in Image processing, Noise, Mean squared error, Exponential function and Signal processing.
His Wavelet research is multidisciplinary, relying on both Subspace topology, Mathematical analysis, Explicit formulae and Spouge's approximation.
In his research, Linear interpolation is intimately related to Spline interpolation, which falls under the overarching field of Mathematical optimization.
He combines subjects such as Active contour model, B-spline, Piecewise and Interpolation with his study of Spline.
The various areas that he examines in his Piecewise study include Bandlimiting and Signal reconstruction.
His most cited work include:
- Sampling signals with finite rate of innovation (908 citations)
- Interpolation revisited [medical images application] (705 citations)
- A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding (532 citations)
What are the main themes of his work throughout his whole career to date?
Thierry Blu mainly investigates Algorithm, Wavelet, Artificial intelligence, Mathematical optimization and Wavelet transform.
His Algorithm study integrates concerns from other disciplines, such as Basis function, Image processing, Filter bank and Signal processing.
His Wavelet research integrates issues from Mathematical analysis and Pure mathematics.
His studies in Artificial intelligence integrate themes in fields like Computer vision and Pattern recognition.
His research integrates issues of Iterated function, Spline interpolation, Interpolation, Mean squared error and Applied mathematics in his study of Mathematical optimization.
His research investigates the connection between Applied mathematics and topics such as Signal reconstruction that intersect with issues in Bandlimiting.
He most often published in these fields:
- Algorithm (39.42%)
- Wavelet (27.80%)
- Artificial intelligence (22.41%)
What were the highlights of his more recent work (between 2014-2020)?
- Algorithm (39.42%)
- Deconvolution (7.05%)
- Artificial intelligence (22.41%)
In recent papers he was focusing on the following fields of study:
Thierry Blu spends much of his time researching Algorithm, Deconvolution, Artificial intelligence, Computer vision and Mathematical optimization.
His biological study spans a wide range of topics, including Parametric statistics, Minification, Image registration, Geometric transformation and System of linear equations.
His Deconvolution study also includes
- Linear combination which connect with Microscopy,
- Point spread function that intertwine with fields like Basis function and Image,
- Wiener filter which intersects with area such as Image restoration and Wiener deconvolution.
His research investigates the connection between Artificial intelligence and topics such as Trajectory that intersect with issues in Sampling, Focus, Position and Global Positioning System.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Grid and Least squares, Non-linear least squares.
His Noise reduction study combines topics from a wide range of disciplines, such as Wavelet and Gaussian noise.
Between 2014 and 2020, his most popular works were:
- A Novel SURE-Based Criterion for Parametric PSF Estimation (48 citations)
- Towards Generalized FRI Sampling With an Application to Source Resolution in Radioastronomy (45 citations)
- Resting State EEG-based biometrics for individual identification using convolutional neural networks (43 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Artificial intelligence
- Statistics
Thierry Blu mainly focuses on Algorithm, Deconvolution, Linear combination, Pixel and Mathematical optimization.
His work carried out in the field of Algorithm brings together such families of science as Adaptive optics, Image registration, Point source, Transformation and Noise measurement.
He interconnects Wiener filter and Point spread function in the investigation of issues within Deconvolution.
His study focuses on the intersection of Linear combination and fields such as Microscopy with connections in the field of Basis function, Calibration and Wavelet transform.
His Mathematical optimization research includes elements of Sampling, Parametric statistics, Signal reconstruction, Noise and Cramér–Rao bound.
His Wiener deconvolution study combines topics in areas such as Computer vision and Artificial intelligence.
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