Miguel Simões
Computer Vision and Pattern Recognition · 2014–2015
A researcher in the Science corpus: 2 papers; trusts 13 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 40 citations into it; #9,471 of 21,575 by trust here, #11,721 by citations
- Media Technology — 40 citations into it; #1,266 of 3,621 by trust here, #1,329 by citations
- Computational Mechanics — 12 citations into it
- Mathematical Physics — 5 citations into it
Whom Miguel Simões trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Patrizio Campisi
- David L. Donoho
- Zhou Wang
- Patrick L. Combettes
- José M. Bioucas-Dias
- Bruno Aiazzi
- Manya V. Afonso
- Gene H. Golub
- Antonin Chambolle
- A. Bovik
Who trusts Miguel Simões overall
- Renwei Dian
- Shutao Li
- Leyuan Fang
- Naoto Yokoya
- Qi Wei
- Charis Lanaras
- Nicolas Dobigeon
- Claas Grohnfeldt
- Emmanuel Baltsavias
- Jocelyn Chanussot
Papers from Miguel Simões's perspective
Ordered by how strongly the researchers Miguel Simões trusts endorse each paper. Same corpus, a different order for every researcher.
- A wavelet tour of signal processing
- A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Nonlinear total variation based noise removal algorithms
- A new look at IHS-like image fusion methods
- Signal Recovery by Proximal Forward-Backward Splitting
- A universal image quality index (IEEE Signal Processing Letters)
- The Laplacian Pyramid as a Compact Image Code
- ImageNet classification with deep convolutional neural networks
- Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries (IEEE Transactions on Image Processing)
Their papers
- Hyperspectral Pansharpening: A Review (2015) — 778 citations
- A Convex Formulation for Hyperspectral Image Superresolution via Subspace-Based Regularization (2014) — 761 citations