Fusion of Low-Quality Visible and Infrared Images Based on Multi-Level Latent Low-Rank Representation Joint With Retinex Enhancement and Multi-Visual Weight Information
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2021.3139670
Abstract: Latent low-rank representation has been applied to multi-level image decomposition for the fusion of infrared and visible images to obtain good results. However, when the original infrared and visible images are of low quality, the… read more here.
Keywords: rank representation; fusion; low rank; multi level ... See more keywords
Hyperspectral Image Reconstruction by Latent Low-Rank Representation for Classification
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2018.2844555
Abstract: To effectively reduce the spectral variation that degrades classification performance, a novel low-rank subspace recovery method based on latent low-rank representation (LatLRR) is proposed for hyperspectral images in this letter. Different from the robust principal… read more here.
Keywords: rank representation; classification; latent low; low rank ... See more keywords
Fast and Latent Low-Rank Subspace Clustering for Hyperspectral Band Selection
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2019.2959342
Abstract: This article presents a fast and latent low-rank subspace clustering (FLLRSC) method to select hyperspectral bands. The FLLRSC assumes that all the bands are sampled from a union of latent low-rank independent subspaces and formulates… read more here.
Keywords: fast latent; low rank; subspace clustering; latent low ... See more keywords
Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
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DOI: 10.1155/2017/1096028
Abstract: Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent… read more here.
Keywords: rank representation; latent low; low rank; dual graph ... See more keywords