What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Statistics
His primary areas of investigation include Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Image processing.
In general Artificial intelligence, his work in Noise reduction, Video denoising and Discrete cosine transform is often linked to Collaborative filtering linking many areas of study.
Encoder, Stereoscopy and Depth perception is closely connected to Video quality in his research, which is encompassed under the umbrella topic of Computer vision.
His Algorithm research is multidisciplinary, incorporating elements of Pointwise, Mathematical optimization and Noise.
Karen O. Egiazarian studies Pattern recognition, focusing on Sparse approximation in particular.
His work carried out in the field of Image processing brings together such families of science as Deconvolution, Computer graphics and Kernel.
His most cited work include:
- Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering (5513 citations)
- NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (669 citations)
- Image database TID2013 (574 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Algorithm, Computer vision, Pattern recognition and Noise reduction.
His Artificial intelligence research includes themes of Filter and Noise.
His studies deal with areas such as Speech recognition, Mathematical optimization, Wavelet transform and Signal processing as well as Algorithm.
His Computer vision study is mostly concerned with Image quality, Data compression, Image, Image compression and Digital image processing.
He interconnects Artificial neural network and Non-local means in the investigation of issues within Pattern recognition.
His research investigates the connection between Noise reduction and topics such as Gaussian noise that intersect with problems in Noise measurement and Salt-and-pepper noise.
He most often published in these fields:
- Artificial intelligence (47.98%)
- Algorithm (32.14%)
- Computer vision (31.99%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (47.98%)
- Pattern recognition (21.58%)
- Noise reduction (16.46%)
In recent papers he was focusing on the following fields of study:
Karen O. Egiazarian focuses on Artificial intelligence, Pattern recognition, Noise reduction, Computer vision and Algorithm.
In his study, which falls under the umbrella issue of Artificial intelligence, Image quality is strongly linked to Metric.
His Pattern recognition study combines topics from a wide range of disciplines, such as Visualization and Noise.
The study incorporates disciplines such as Hyperspectral imaging, Filter, Grayscale, Additive white Gaussian noise and Signal processing in addition to Noise reduction.
Lossless compression, Image, Reference image, Non-local means and Image processing are the core of his Computer vision study.
Karen O. Egiazarian has included themes like Theoretical computer science and Iterative reconstruction in his Algorithm study.
Between 2015 and 2021, his most popular works were:
- NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (669 citations)
- Dominant Rotated Local Binary Patterns (DRLBP) for texture classification (118 citations)
- Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Statistics
Karen O. Egiazarian mainly focuses on Artificial intelligence, Pattern recognition, Noise reduction, Computer vision and Algorithm.
The various areas that Karen O. Egiazarian examines in his Artificial intelligence study include Metric and Noise.
The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network, Visualization and Invariant.
His Noise reduction research incorporates themes from Hyperspectral imaging, Filter, Noise, Contextual image classification and Pattern recognition.
His studies in Computer vision integrate themes in fields like Phase imaging and Content adaptive.
His Algorithm research includes elements of Matching, Noise suppression and Iterative reconstruction.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
