What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical analysis
- Statistics
His primary areas of investigation include Algorithm, Artificial intelligence, Wavelet, Spline and Computer vision.
The concepts of his Algorithm study are interwoven with issues in Mean squared error, Mathematical optimization, Resolution and Signal reconstruction.
Michael Unser interconnects Iterative method and Pattern recognition in the investigation of issues within Artificial intelligence.
As part of the same scientific family, Michael Unser usually focuses on Wavelet, concentrating on Mathematical analysis and intersecting with Applied mathematics, Gabor wavelet and Pure mathematics.
His Spline research is multidisciplinary, relying on both Discrete mathematics, Digital filter, Basis function, B-spline and Spline interpolation.
His Computer vision research is multidisciplinary, incorporating perspectives in Pairwise comparison and Microscopy.
His most cited work include:
- A pyramid approach to subpixel registration based on intensity (2054 citations)
- Splines: a perfect fit for signal and image processing (1395 citations)
- Texture classification and segmentation using wavelet frames (1231 citations)
What are the main themes of his work throughout his whole career to date?
Algorithm, Artificial intelligence, Wavelet, Computer vision and Spline are his primary areas of study.
His Algorithm study incorporates themes from Basis function, Inverse problem, Mathematical optimization and Iterative reconstruction.
Artificial intelligence connects with themes related to Pattern recognition in his study.
His work investigates the relationship between Wavelet and topics such as Mathematical analysis that intersect with problems in Applied mathematics.
Computer vision is closely attributed to Medical imaging in his study.
The study incorporates disciplines such as Parametric statistics, Smoothing spline, Spline interpolation, Thin plate spline and B-spline in addition to Spline.
He most often published in these fields:
- Algorithm (35.60%)
- Artificial intelligence (31.36%)
- Wavelet (25.56%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (35.60%)
- Artificial intelligence (31.36%)
- Inverse problem (7.37%)
In recent papers he was focusing on the following fields of study:
Michael Unser focuses on Algorithm, Artificial intelligence, Inverse problem, Iterative reconstruction and Regularization.
Michael Unser specializes in Algorithm, namely Iterative method.
His Artificial intelligence study integrates concerns from other disciplines, such as Computer vision and Pattern recognition.
His Inverse problem study also includes
- Applied mathematics, which have a strong connection to Spline, Quadratic equation and Interpolation,
- Optics together with Phase retrieval.
His Iterative reconstruction research is multidisciplinary, incorporating elements of Deconvolution, Optimization problem, Image resolution and Compressed sensing.
The concepts of his Wavelet study are interwoven with issues in Mathematical analysis and White noise.
Between 2015 and 2021, his most popular works were:
- Deep Convolutional Neural Network for Inverse Problems in Imaging (930 citations)
- Convolutional Neural Networks for Inverse Problems in Imaging: A Review (306 citations)
- DeconvolutionLab2: An open-source software for deconvolution microscopy. (205 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Mathematical analysis
Michael Unser mostly deals with Algorithm, Inverse problem, Artificial intelligence, Iterative reconstruction and Regularization.
His Algorithm research incorporates themes from Tomography, Projector, Optics and Parametric statistics.
His Inverse problem study incorporates themes from Operator, Combinatorics, Applied mathematics, Optimization problem and Compressed sensing.
His study in Artificial intelligence focuses on Wavelet in particular.
His Iterative reconstruction study combines topics from a wide range of disciplines, such as Deconvolution, Oblique projection, Minification, Convolution and Convolutional neural network.
His work in Regularization covers topics such as Subspace topology which are related to areas like Spline interpolation.
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