What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Optics
Gonzalo R. Arce focuses on Artificial intelligence, Algorithm, Computer vision, Image processing and Median filter.
The study incorporates disciplines such as Nonlinear methods, Computer engineering and Pattern recognition in addition to Artificial intelligence.
His Algorithm study combines topics from a wide range of disciplines, such as Theoretical computer science, Filter, Filter design and Control theory.
As a member of one scientific family, he mostly works in the field of Computer vision, focusing on Decoding methods and, on occasion, Robustness, Human visual system model, Color image, Visual comparison and Luminance.
The Image processing study combines topics in areas such as Nonlinear theory, Spectral imaging, Coded aperture and Digital watermarking.
His study looks at the relationship between Median filter and fields such as Linear filter, as well as how they intersect with chemical problems.
His most cited work include:
- Nonlinear Signal Processing: A Statistical Approach (352 citations)
- A multiresolution watermark for digital images (349 citations)
- Halftone visual cryptography (321 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Algorithm, Optics, Computer vision and Spectral imaging.
Gonzalo R. Arce regularly links together related areas like Pattern recognition in his Artificial intelligence studies.
His work in Algorithm addresses issues such as Signal processing, which are connected to fields such as Nonlinear system.
His Optics study incorporates themes from Image quality and Inverse problem.
Many of his studies on Computer vision apply to Visual cryptography as well.
His Spectral imaging research incorporates elements of Hyperspectral imaging, Imaging spectroscopy, Aperture and Coded aperture.
He most often published in these fields:
- Artificial intelligence (32.89%)
- Algorithm (31.57%)
- Optics (28.26%)
What were the highlights of his more recent work (between 2017-2021)?
- Optics (28.26%)
- Algorithm (31.57%)
- Coded aperture (17.88%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Optics, Algorithm, Coded aperture, Compressed sensing and Spectral imaging.
His study in Algorithm is interdisciplinary in nature, drawing from both Image quality, Matrix, Bandlimiting, Colors of noise and Convolution.
His studies deal with areas such as Tomography, Hyperspectral imaging, Iterative reconstruction and Data cube as well as Coded aperture.
The concepts of his Compressed sensing study are interwoven with issues in Computational complexity theory and Sparse approximation.
The various areas that Gonzalo R. Arce examines in his Spectral imaging study include Pixel, Artificial intelligence, Cardinal point, Temporal resolution and Adaptive coding.
His Artificial intelligence study integrates concerns from other disciplines, such as Spatial analysis and Pattern recognition.
Between 2017 and 2021, his most popular works were:
- Fast optical proximity correction method based on nonlinear compressive sensing. (16 citations)
- Compressive spectral imaging system based on liquid crystal tunable filter (14 citations)
- A Doubly Orthogonal Matching Pursuit Algorithm for Sparse Predistortion of Power Amplifiers (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Optics
- Algorithm
His primary scientific interests are in Optics, Coded aperture, Algorithm, Compressed sensing and Spectral imaging.
His Optics research is multidisciplinary, incorporating perspectives in Inverse problem and Thresholding.
His Coded aperture research includes themes of Hyperspectral imaging and Iterative reconstruction.
His work deals with themes such as Dither, Point spread function and Amplifier, Predistortion, which intersect with Algorithm.
Gonzalo R. Arce has included themes like Pixel, Computer vision, Artificial intelligence and Colors of noise in his Spectral imaging study.
His study in the field of Image sensor is also linked to topics like Colored.
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