Results 11 to 20 of about 155,579 (310)
Deconvolution with shapelets [PDF]
Astronomy & Astrophysics, 2008 We seek to find a shapelet-based scheme for deconvolving galaxy images from the PSF which leads to unbiased shear measurements. Based on the analytic formulation of convolution in shapelet space, we construct a procedure to recover the unconvolved shapelet coefficients under the assumption that the PSF is perfectly known. Using specific simulations, we
Melchior, Peter +3 more
openaire +2 more sources
Entropy, 2022 To address the difficulty of extracting the features of composite-fault signals under a low signal-to-noise ratio and complex noise conditions, a feature-extraction method based on phase-space reconstruction and maximum correlation Re’nyi entropy ...
Zhen Zhang +3 more
doaj +1 more source
GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA
Nucleic Acids Res., 2021 In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on the bulk gene expression datasets in the TCGA and the GTEx projects, providing the biologists and clinicians with a ...
Chenwei Li +4 more
semanticscholar +1 more source
DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit ...
Jiangxin Dong, S. Roth, B. Schiele
semanticscholar +1 more source
Journal of Synchrotron Radiation, 2022 Resonant inelastic X-ray scattering (RIXS) has become an important scientific tool. Nonetheless, conventional high-resolution (few hundred meV or less) RIXS measurements, especially in the soft X-ray range, require low-throughput grating spectrometers ...
Daniel J. Higley +3 more
doaj +1 more source
Nature Communications, 2022 Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell
P. Danaher +6 more
semanticscholar +1 more source
Exploiting the point spread function for optical imaging through a scattering medium based on deconvolution method [PDF]
Journal of Innovative Optical Health Sciences, 2019 Visual perception of humans penetrating turbid medium is hampered by scattering. Various techniques have been prompted recently to recover optical imaging through turbid materials. Among them, speckle correlation based on deconvolution is one of the most
Hexiang He +4 more
doaj +1 more source
Optics Express, 2012 The ability to retrieve information from different layers within a stratified sample using terahertz pulsed reflection imaging and spectroscopy has traditionally been resolution limited by the pulse width available. In this paper, a deconvolution algorithm is presented which circumvents this resolution limit, enabling deep sub-wavelength and sub-pulse ...
Gillian C. Walker +7 more
openaire +2 more sources
Blind Hierarchical Deconvolution [PDF]
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020 Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of model-based inversion techniques require knowledge on the convolution kernel to recover an accurate reconstruction and additionally prior assumptions on the regularity of the ...
Arttu Arjas +3 more
openaire +3 more sources
SpatialDWLS: accurate deconvolution of spatial transcriptomic data
Genome Biology, 2021 Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here,
Rui Dong, Guocheng Yuan
semanticscholar +1 more source