His primary areas of study are Artificial intelligence, Pattern recognition, Machine learning, Background subtraction and Algorithm. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Computer vision. The various areas that Deyu Meng examines in his Pattern recognition study include Noise reduction, Iterative reconstruction and Noise.
As a part of the same scientific study, he usually deals with the Machine learning, concentrating on Weighting and frequently concerns with Multimedia search, Heuristic, Continuous function, Classifier and Training set. Deyu Meng combines subjects such as Robust principal component analysis, Norm and Mathematical optimization with his study of Background subtraction. In his work, Iterated function, Thresholding and Contextual image classification is strongly intertwined with Image restoration, which is a subfield of Algorithm.
Deyu Meng focuses on Artificial intelligence, Pattern recognition, Machine learning, Deep learning and Algorithm. His Artificial intelligence study frequently draws connections to other fields, such as Computer vision. Deyu Meng has included themes like Regularization, Noise reduction and Feature in his Pattern recognition study.
His work on Semi-supervised learning, Leverage and Supervised learning as part of general Machine learning research is frequently linked to Self paced, bridging the gap between disciplines. His studies deal with areas such as Subspace topology, Mathematical optimization, Iterative reconstruction and Tensor as well as Algorithm. His studies in Subspace topology integrate themes in fields like Mixture model and Optimization problem.
Artificial intelligence, Pattern recognition, Deep learning, Machine learning and Image are his primary areas of study. His Artificial intelligence study combines topics in areas such as Generalization and Computer vision. His biological study spans a wide range of topics, including Regularization, Noise reduction and Residual.
Noise is closely connected to Image restoration in his research, which is encompassed under the umbrella topic of Deep learning. His Machine learning research integrates issues from Generative model and Benchmark. Many of his research projects under Image are closely connected to Field with Field, tying the diverse disciplines of science together.
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Image and Hyperspectral imaging. As a member of one scientific family, Deyu Meng mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Classifier. His Pattern recognition study combines topics from a wide range of disciplines, such as Artificial neural network, Noise reduction and Image resolution.
His work deals with themes such as Ground truth, Voxel and Atlas, which intersect with Deep learning. His work on Inpainting as part of general Image study is frequently linked to Scale, therefore connecting diverse disciplines of science. His research investigates the connection with Hyperspectral imaging and areas like Multispectral image which intersect with concerns in Rank, Generative model and Image restoration.
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.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Kai Zhang;Wangmeng Zuo;Yunjin Chen;Deyu Meng.
IEEE Transactions on Image Processing (2017)
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision
Shuhang Gu;Qi Xie;Deyu Meng;Wangmeng Zuo.
International Journal of Computer Vision (2017)
Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework
Dingwen Zhang;Deyu Meng;Junwei Han.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
Convolutional Sparse Coding for Image Super-Resolution
Shuhang Gu;Wangmeng Zuo;Qi Xie;Deyu Meng.
international conference on computer vision (2015)
Self-paced curriculum learning
Lu Jiang;Deyu Meng;Qian Zhao;Shiguang Shan.
national conference on artificial intelligence (2015)
A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding
Wangmeng Zuo;Deyu Meng;Lei Zhang;Xiangchu Feng.
international conference on computer vision (2013)
Infrared Patch-Image Model for Small Target Detection in a Single Image
Chenqiang Gao;Deyu Meng;Yi Yang;Yongtao Wang.
IEEE Transactions on Image Processing (2013)
Self-Paced Learning with Diversity
Lu Jiang;Deyu Meng;Shoou-I Yu;Zhenzhong Lan.
neural information processing systems (2014)
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising
Yi Peng;Deyu Meng;Zongben Xu;Chenqiang Gao.
computer vision and pattern recognition (2014)
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
Jiang Liu;Chenqiang Gao;Deyu Meng;Alexander G. Hauptmann.
computer vision and pattern recognition (2018)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: