What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Guangming Shi mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Sparse approximation and Image processing.
His Artificial intelligence study is mostly concerned with Image restoration, Human visual system model, Pixel, Image quality and Iterative reconstruction.
His Pattern recognition study incorporates themes from Salt-and-pepper noise, Deblurring, Non-local means, Gradient noise and Noise reduction.
The study incorporates disciplines such as Detector, Pascal and Single shot in addition to Computer vision.
His work deals with themes such as Hyperspectral imaging, Full spectral imaging, Cluster analysis and Image texture, which intersect with Sparse approximation.
The Image processing study combines topics in areas such as Transform coding, Wavelet transform and Rate–distortion optimization.
His most cited work include:
- Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (1003 citations)
- Nonlocally Centralized Sparse Representation for Image Restoration (991 citations)
- Two-stage image denoising by principal component analysis with local pixel grouping (491 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Compressed sensing are his primary areas of study.
His is involved in several facets of Artificial intelligence study, as is seen by his studies on Pixel, Human visual system model, Image quality, Iterative reconstruction and Image processing.
His Computer vision study frequently draws connections to adjacent fields such as Perception.
His Pattern recognition research includes elements of Visualization and Image restoration.
Guangming Shi works on Image restoration which deals in particular with Deblurring.
The concepts of his Algorithm study are interwoven with issues in Filter bank, Coding, Mathematical optimization and Filter design.
He most often published in these fields:
- Artificial intelligence (64.11%)
- Computer vision (36.39%)
- Pattern recognition (29.95%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (64.11%)
- Pattern recognition (29.95%)
- Computer vision (36.39%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Deep learning.
His work in Image quality, Human visual system model, Compressed sensing, Feature extraction and Feature is related to Artificial intelligence.
In Compressed sensing, Guangming Shi works on issues like Image, which are connected to Neural coding.
He works in the field of Pattern recognition, focusing on Discriminative model in particular.
His Computer vision research includes themes of Perception and Detector.
He works mostly in the field of Deep learning, limiting it down to topics relating to Noise reduction and, in certain cases, Probabilistic logic, Energy and Algorithm.
Between 2017 and 2021, his most popular works were:
- Denoising Prior Driven Deep Neural Network for Image Restoration (115 citations)
- Feature-fused SSD: fast detection for small objects (50 citations)
- A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Deep learning.
All of his Artificial intelligence and Image quality, Compressed sensing, Feature extraction, Feature and Image resolution investigations are sub-components of the entire Artificial intelligence study.
His Pattern recognition study integrates concerns from other disciplines, such as RGB color model, Perspective, Image and Iterative method.
His studies in Computer vision integrate themes in fields like Pascal and Detector.
The Deep learning study combines topics in areas such as Noise reduction and Demosaicing.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Image restoration and Deblurring.
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