Results 51 to 60 of about 724,897 (308)
Efficient Tuning-Free l1-Regression of Nonnegative Compressible Signals
Frontiers in Applied Mathematics and Statistics, 2021 In compressed sensing the goal is to recover a signal from as few as possible noisy, linear measurements with the general assumption that the signal has only a few non-zero entries.
Hendrik Bernd Petersen +3 more
doaj +1 more source
Iterative thresholding for sparse approximations [PDF]
, 2008 Sparse signal expansions represent or approximate a signal using a small number of elements from a large collection of elementary waveforms. Finding the optimal sparse expansion is known to be NP hard in general and non-optimal strategies such as ...
Blumensath, T. +3 more
core +1 more source
Hyperspectral Image Denoising via
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingThis article studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the $L_{1}$-norm-based regularizers to remove sparse noise and ...
Xin Tian, Kun Xie, Hanling Zhang
doaj +1 more source
Remote SensingEfficiently removing clouds from remote sensing imagery presents a significant challenge, yet it is crucial for a variety of applications. This paper introduces a novel sparse function, named the tri-fiber-wise sparse function, meticulously engineered ...
Dong-Lin Sun, Teng-Yu Ji, Meng Ding
doaj +1 more source
Sparse Image Reconstruction using Sparse Priors [PDF]
2006 International Conference on Image Processing, 2006 Sparse image reconstruction is of interest in the fields of radioastronomy and molecular imaging. The observation is assumed to be a linear transformation of the image, and corrupted by additive white Gaussian noise. We study the usage of sparse priors in the empirical Bayes framework: it permits the selection of the hyperparameters of the prior in a ...
Michael Ting +2 more
openaire +1 more source
sparse-lm: Sparse linear regression models in Python
, 2023 <p>* JOSS publication archived version</p><p> </p><p><!-- Release notes generated using configuration in .github/release.yml at v0.5.2 --></p><h2>What's Changed</h2><h3> Tests</h3><
Luis Barroso-Luque, Fengyu Xie
core +1 more source
Identifying Patterns of Hospitalizations Among Childhood Cancer Survivors: A Population‐Based Study
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Childhood cancer survivors (CCSs) experience more hospitalizations than the general population. We aimed to identify distinct hospitalization patterns among Queensland CCSs and the associated characteristics. Methods Cases (aged ≤14 years) diagnosed between 1995 and 2019 while residents of Queensland were identified through the ...
Hai Pham +7 more
wiley +1 more source
Identification of Matrices Having a Sparse Representation [PDF]
, 2008 We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary.
Pfander, Goetz E. +6 more
core +1 more source
CMOS Fixed Pattern Noise Elimination Based on Sparse Unidirectional Hybrid Total Variation
Sensors, 2020 With the improvement of semiconductor technology, the performance of CMOS Image Sensor has been greatly improved, reaching the same level as that of CCD in dark current, linearity and readout noise. However, due to the production process, CMOS has higher
Tao Zhang +3 more
doaj +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Heavy menstrual bleeding (HMB) is a common condition among adolescents and young adults (AYAs) and is the leading cause of iron deficiency anemia (IDA) in this population. Growing evidence suggests that social determinants of health (SDOH) influence menstrual bleeding patterns, access to care, and treatment. However, no studies have
Maria Carter‐Febres +6 more
wiley +1 more source