Missing data reconstruction in VHR images based on progressive structure prediction and texture generation
Sign Up to like & getrecommendations! Published in 2021 at "Isprs Journal of Photogrammetry and Remote Sensing"
DOI: 10.1016/j.isprsjprs.2020.11.020
Abstract: Abstract Very high resolution (VHR) satellite and aerial images often suffer from scene occlusion caused by redundant objects. The task of removing these redundant objects can be solved by missing data reconstruction technology. However, when… read more here.
Keywords: reconstruction; data reconstruction; generation; vhr images ... See more keywords
Kriging based sequence interpolation and probability distribution correction for gaussian wind field data reconstruction
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Wind Engineering and Industrial Aerodynamics"
DOI: 10.1016/j.jweia.2020.104340
Abstract: Abstract Data reconstruction is an important research topic for missing data recovery and data supplement. Spatial interpolation is often used for data reconstruction. The interpolation for time series is usually conducted at each time point… read more here.
Keywords: reconstruction; wind; probability; data reconstruction ... See more keywords
Hardware Engineering for an APT in a TEM Objective Lens
Sign Up to like & getrecommendations! Published in 2020 at "Microscopy and Microanalysis"
DOI: 10.1017/s1431927620022187
Abstract: The most widespread APT data reconstruction method was first proposed in 1995 [1]. Incremental changes to the method have lead to improved accuracies as datasets became larger, laser field evaporation became more widespread, and heterogeneous… read more here.
Keywords: reconstruction; data reconstruction; field; tem objective ... See more keywords
Nonconvex Log-Sum Function-Based Majorization–Minimization Framework for Seismic Data Reconstruction
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2909776
Abstract: Because of the fact that complete seismic data can have a low rank in the frequency-space (f-x) domain, rank-reduction methods are classical techniques used for seismic data reconstruction. Models that employ nuclear-norm minimization signify convex… read more here.
Keywords: data reconstruction; seismic data; minimization; function ... See more keywords
Seismic Data Reconstruction Using Deep Bidirectional Long Short-Term Memory With Skip Connections
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.2993847
Abstract: Due to environmental and economic constraints on their acquisition, seismic data are always irregularly sampled and include bad or missing traces, which can cause problems for seismic data processing. Recently, many researchers have attempted to… read more here.
Keywords: data reconstruction; seismic data; reconstruction using; deep bidirectional ... See more keywords
Seismic Data Reconstruction via Wavelet-Based Residual Deep Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3152984
Abstract: Seismic data reconstruction is one of the essential steps in the seismic data processing. Recently, the deep learning (DL) models have attracted huge attention in seismic exploration, which has been applied to seismic data reconstruction,… read more here.
Keywords: deep learning; seismic data; loss; wavelet ... See more keywords
Seismic Data Reconstruction and Denoising by Enhanced Hankel Low-Rank Matrix Estimation
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2023.3267941
Abstract: Seismic data reconstruction and denoising play a fundamental role in most seismic data processing algorithms which are often designed for regularly sampled and reliable data. Using the fact that the (block) Hankel matrix formulated from… read more here.
Keywords: low rank; reconstruction; reconstruction denoising; data reconstruction ... See more keywords
Unsupervised Feature Selection Via Data Reconstruction and Side Information
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2020.3011253
Abstract: Data reconstruction, which aims at preserving statistical properties of the data during the reconstruction has become a new criterion for feature selection. Although feature selection could benefit from the perspective of data reconstruction, it is… read more here.
Keywords: data reconstruction; feature selection; information;
Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach
Sign Up to like & getrecommendations! Published in 2019 at "Structural Health Monitoring"
DOI: 10.1177/1475921719844039
Abstract: Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the… read more here.
Keywords: reconstruction; compressive sensing; health monitoring; data reconstruction ... See more keywords
Data reconstruction can improve abundance index estimation: An example using Taiwanese longline data for Pacific bluefin tuna
Sign Up to like & getrecommendations! Published in 2017 at "PLoS ONE"
DOI: 10.1371/journal.pone.0185784
Abstract: Catch-per-unit-effort (CPUE) is often the main piece of information used in fisheries stock assessment; however, the catch and effort data that are traditionally compiled from commercial logbooks can be incomplete or unreliable due to many… read more here.
Keywords: information; taiwanese longline; data reconstruction; bluefin tuna ... See more keywords
A Missing Data Reconstruction Method Using an Accelerated Least-Squares Approximation with Randomized SVD
Sign Up to like & getrecommendations! Published in 2022 at "Algorithms"
DOI: 10.3390/a15060190
Abstract: An accelerated least-squares approach is introduced in this work by incorporating a greedy point selection method with randomized singular value decomposition (rSVD) to reduce the computational complexity of missing data reconstruction. The rSVD is used… read more here.
Keywords: missing data; accelerated least; method; least squares ... See more keywords