Results 91 to 100 of about 750,933 (209)
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
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Bayesian Image Super-resolution, Continued
, 2007 This paper develops a multi-frame image super-resolution approach from a Bayesian view-point by marginalizing over the unknown registration parameters relating the set of input low-resolution views. In Tipping and Bishop’s Bayesian image super-resolution approach [16], the marginalization was over the super- resolution image, necessitating the use of ...
Pickup, LC +3 more
openaire +2 more sources
Selective Image Super-Resolution
, 2010 In this paper we propose a vision system that performs image Super Resolution (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed "blind" resolution recovery to the entire image area.
Sun, Ju +3 more
openaire +2 more sources
Resolution Enhancement of Brain MRI Images Using Deep Learning
Engineering ProceedingsOne of the most widely used imaging techniques in medicine is magnetic resonance imaging (MRI). It is a tool that doctors use to comprehend human anatomy and carry out more accurate analyses.
Minakshi Roy +3 more
doaj +1 more source
Semantic-Aware Guided Low-Light Image Super-Resolution
IEEE AccessThe single image super-resolution based on deep learning has achieved extraordinary performance. However, due to inevitable environmental or technological limitations, some images not only have low resolution but also low brightness.
Sheng Ren +3 more
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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 ...
Pickup Lyndsey C +3 more
doaj
Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding
British Machine Vision Conference, 2012 M. Bevilacqua +3 more
semanticscholar +1 more source