What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
Xiaolin Wu mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Data compression.
Artificial intelligence is represented through his Lossless compression, Image restoration, Image processing, Image resolution and Image quality research.
Xiaolin Wu has researched Lossless compression in several fields, including Codec, Piecewise and Autoregressive model.
His Pattern recognition research is multidisciplinary, incorporating elements of Transform coding, JPEG, Digital signal and Deblurring.
His work in Algorithm addresses issues such as Edge detection, which are connected to fields such as Multiplication, Scale-space segmentation and Segmentation-based object categorization.
His Data compression research is multidisciplinary, incorporating perspectives in Lossy compression, Image compression, Quantization and Theoretical computer science.
His most cited work include:
- Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (1003 citations)
- Context-based, adaptive, lossless image coding (936 citations)
- An edge-guided image interpolation algorithm via directional filtering and data fusion (690 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Data compression.
His Image restoration, Pixel, Image resolution, Wavelet and Demosaicing investigations are all subjects of Artificial intelligence research.
As part of his studies on Computer vision, Xiaolin Wu often connects relevant areas like Interpolation.
His Algorithm research integrates issues from Theoretical computer science, Mathematical optimization and Coding.
His research integrates issues of Lossy compression, Image compression and Wavelet transform in his study of Data compression.
The Image compression study combines topics in areas such as Transform coding and Codec.
He most often published in these fields:
- Artificial intelligence (56.60%)
- Computer vision (37.56%)
- Algorithm (26.90%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (56.60%)
- Computer vision (37.56%)
- Image restoration (11.68%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Image restoration, Pattern recognition and Convolutional neural network.
His study in Deep learning, Image, Artificial neural network, Pixel and Image resolution is carried out as part of his Artificial intelligence studies.
His studies examine the connections between Computer vision and genetics, as well as such issues in Computer graphics, with regards to Image formation and Digital Light Processing.
His Image restoration course of study focuses on Histogram and Image segmentation and Backlight.
The study incorporates disciplines such as Lossless compression and Inverse problem in addition to Pattern recognition.
His work carried out in the field of Algorithm brings together such families of science as Theoretical computer science and Template matching.
Between 2013 and 2021, his most popular works were:
- Random Walk Graph Laplacian-Based Smoothness Prior for Soft Decoding of JPEG Images (86 citations)
- Generalized Equalization Model for Image Enhancement (71 citations)
- Data-driven sparsity-based restoration of JPEG-compressed images in dual transform-pixel domain (65 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Computer vision
His primary areas of study are Artificial intelligence, Computer vision, Image restoration, Pattern recognition and Pixel.
His study brings together the fields of Backlight and Artificial intelligence.
As part of one scientific family, Xiaolin Wu deals mainly with the area of Computer vision, narrowing it down to issues related to the Frame, and often Communication channel, Visible light communication and Video processing.
The concepts of his Image restoration study are interwoven with issues in Image resolution, Image gradient and Inverse problem.
His Pattern recognition research incorporates themes from Graph, Laplacian matrix and Iterative reconstruction.
His Pixel research includes elements of Image sensor, Analog signal, Interference, Latent image and Moiré pattern.
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