Results 41 to 50 of about 18,250,440 (204)
Generative collaborative networks for single image super-resolution [PDF]
Neurocomputing, 2020 A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to the choice of the objective loss function. In particular, recent works proposed the use of a VGG loss which consists
Mohamed El Amine Seddik +2 more
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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
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Single-image super-resolution improvement of X-ray single-particle diffraction images using convolutional neural network [PDF]
, 2021 Femtosecond X-ray pulse lasers are promising probes for elucidating the multi-conformational states of biomolecules because they enable snapshots of single biomolecules to be observed as coherent diffraction images.
Yoshinobu, Akinaga +3 more
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Super-Resolution Microscopy: A Virus’ Eye View of the Cell [PDF]
, 2014 It is difficult to observe the molecular choreography between viruses and host cell components, as they exist on a spatial scale beyond the reach of conventional microscopy.
Grove, J, Grove, Joe, Joe Grove
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Deep Back-ProjectiNetworks for Single Image Super-Resolution [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images.
Muhammad Haris 0002 +2 more
openaire +3 more sources
Single-shot super-resolution quantitative phase imaging allowed by coherence gate shaping
, 2023 Biomedical and metasurface researchers repeatedly reach for quantitative phase imaging (QPI) as their primary imaging technique due to its high-throughput, label-free, quantitative nature. So far, very little progress has been made toward achieving super-
Petr Bouchal +5 more
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Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks
Remote Sensing, 2020 Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data ...
Francesco Salvetti +3 more
doaj +1 more source
IEEE Access, 2021 In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high ...
Fei Wang, Mali Gong
doaj +1 more source
Local semi-supervised regression for single-image super-resolution
, 2011 In this paper, we propose a local semi-supervised learning-based algorithm for single-image super-resolution. Different from most of example-based algorithms, the information of test patches is considered during learning local regression functions which ...
Xiaoli Pan +17 more
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Clustering-Oriented Multiple Convolutional Neural Networks for Single Image Super-Resolution [PDF]
, 2017 This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural ...
Ren, Peng +7 more
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