Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise

Autor: Kevin Bui, Yifei Lou, Fredrick Park, Jack Xin

Publikováno v: Frontiers in Computer Science, Vol 5 (2023)

In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by k-means clustering to segment the image. Specifically

Externí odkaz: https://doaj.org/article/9598296776364d1894614672984afcdf

Tensor-based reconstruction applied to regularized time-lapse data

Autor: Jonathan Popa, Susan E Minkoff, Yifei Lou

Publikováno v: Geophysical Journal International. 231:638-649

SUMMARY Repeatedly recording seismic data over a period of months or years is one way to identify trapped oil and gas and to monitor CO2 injection in underground storage reservoirs and saline aquifers. This process of recording data over time and the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bea4f9e03e42efab550f017102019e4a
https://doi.org/10.1093/gji/ggac211

Non-convex approaches for low-rank tensor completion under tubal sampling

Autor: Zheng Tan, Longxiu Huang, HanQin Cai, Yifei Lou

Tensor completion is an important problem in modern data analysis. In this work, we investigate a specific sampling strategy, referred to as tubal sampling. We propose two novel non-convex tensor completion frameworks that are easy to implement, name

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb6e99737575e881d3363647664b72fa

ADAPTIVELY WEIGHTED DIFFERENCE MODEL OF ANISOTROPIC AND ISOTROPIC TOTAL VARIATION FOR IMAGE DENOISING.

Autor: BAOLI SHI, MENGXIA LI, YIFEI LOU

Publikováno v: Journal of Nonlinear & Variational Analysis; 2023, Vol. 7 Issue 4, p563-580, 18p

Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization

Autor: Jocelyn T. Chi, Harlin Lee, Jocelyn Chanussot, Andrea L. Bertozzi, Lucas Drumetz, Jing Qin, Yifei Lou

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing, vol 59, iss 4
IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2021, 59 (4), pp.3338-3351. ⟨10.1109/TGRS.2020.3020810⟩

International audience; Remote sensing data from hyperspectral cameras suffer from limited spatial resolution, in which a single pixel of a hyperspectral image may contain information from several materials in the field of view. Blind hyperspectral i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4da51b824a090376de46cf7026a41b1
https://doi.org/10.1109/tgrs.2020.3020810