Results 101 to 110 of about 10,813,722 (307)
Molecular Oncology, EarlyView.Matched spatial transcriptomics and single‐nuclei RNA‐seq were generated for anaplastic and BRAFV600E papillary thyroid cancers revealing generic and tumor‐specific states occurring in cancer cells and in the tumor microenvironment. In this context, cancer dedifferentiation mirrored organoid maturation through ordered thyroid marker gain/loss ...
Adrien Tourneur +11 more
wiley +1 more source
MSISR-STF: Spatiotemporal Fusion via Multilevel Single-Image Super-Resolution
, 2023 Due to technological limitations and budget constraints, spatiotemporal image fusion uses the complementarity of high temporal–low spatial resolution (HTLS) and high spatial–low temporal resolution (HSLT) data to obtain high temporal and ...
Junqing Fan +5 more
core +1 more source
On using gait to enhance face extraction for visual surveillance
, 2012 Visual surveillance finds increasing deployment for monitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose.
Jung, Sung Uk
core +1 more source
Overcoming Registration Uncertainty in Image Super-Resolution: Maximize or Marginalize?
EURASIP Journal on Advances in Signal Processing, 2007 In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images. This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur) and
Andrew Zisserman +3 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
Local learning-based image super-resolution
, 2011 Local learning algorithm has been widely used in single-frame super-resolution reconstruction algorithm, such as neighbor embedding algorithm [1] and locality preserving constraints algorithm [2].
YuanHaoliang +6 more
core
CEACAM1 participation in breast cancer progression
Molecular Oncology, EarlyView.In invasive breast cancer (BC), CEACAM1 shifts from an apical to a uniform membranous/cytoplasmic pattern, or is lost, as tumors dedifferentiate, inversely tracking the Ki‐67 proliferative index. In MCF‐7 cells, only CEACAM1‐4L suppresses proliferation, repressing cell cycle and growth factor genes.
Mykola Lyndin +3 more
wiley +1 more source
Large receptive field convolutional neural network for image super-resolution
, 2017 This paper presents a new approach to Single Image Super Resolution (SISR), based upon Convolutional Neural Network (CNN). Although the SISR is ill-posed which can be seen as finding a non-linear mapping from a low to high dimensional space.
Wang Q(王强) +3 more
core
Image super‐resolution via dynamic network
CAAI Transactions on Intelligence TechnologyConvolutional neural networks depend on deep network architectures to extract accurate information for image super‐resolution. However, obtained information of these convolutional neural networks cannot completely express predicted high‐quality images ...
Chunwei Tian +4 more
doaj +1 more source
International Journal of Biomedical Imaging, 2018 Given a low-resolution image, there are many challenges to obtain a super-resolved, high-resolution image. Many of those approaches try to simultaneously upsample and deblur an image in signal domain.
Naoki Kawamura +2 more
doaj +1 more source