Results 51 to 60 of about 389,857 (275)
Fast and simple super-resolution with single images
Scientific Reports, 2022 AbstractWe present a fast and simple algorithm for super-resolution with single images. It is based on penalized least squares regression and exploits the tensor structure of two-dimensional convolution. A ridge penalty and a difference penalty are combined; the former removes singularities, while the latter eliminates ringing. We exploit the conjugate
Eilers, Paul H. C., Ruckebusch, Cyril
openaire +4 more sources
Enhanced Deep Residual Networks for Single Image Super-Resolution
, 2017 Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance.
Kim, Heewon +4 more
core +1 more source
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
, 2018 Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical information and is often necessary for accurate quantitative analysis.
Chen, Yuhua +6 more
core +2 more sources
A Very Deep Spatial Transformer Towards Robust Single Image Super-Resolution
IEEE Access, 2019 In general, existing research on single image super-resolution does not consider the practical application that, when image transmission is over noisy channels, the effect of any possible geometric transformations could incur significant quality loss and
Jianmin Jiang +2 more
doaj +1 more source
Arbitrary Scale Super-Resolution Neural Network Based on Residual Channel-Spatial Attention
IEEE Access, 2022 In recent years, the performance of convolutional neural networks in single-image super-resolution has improved significantly. However, most state-of-the-art models address the super-resolution problem for specific scale factors.
Javier Gurrola-Ramos +2 more
doaj +1 more source
Seven ways to improve example-based single image super resolution
, 2015 In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search structures, 3) cascading, 4) image self ...
Rothe, Rasmus +2 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Ongoing evidence indicates increased risk of sarcopenic obesity among children and young people (CYP) with acute lymphoblastic leukemia (ALL), often beginning early in treatment, persisting into survivorship. This review evaluates current literature on body composition in CYP with ALL during and after treatment.
Lina A. Zahed +5 more
wiley +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background B‐acute lymphoblastic leukemia (B‐ALL) is the most common pediatric cancer, and while most children in high‐resource settings are cured, therapy carries risks for long‐term toxicities. Understanding parents’ concerns about these late effects is essential to guide anticipatory support and inform evolving therapeutic approaches ...
Kellee N. Parker +7 more
wiley +1 more source
Geocarto InternationalDespite natural image super-resolution (SR) methods have achieved great success, super-resolution methods for hyperspectral image (HSI) with rich spectral features are still a very challenging task.
Lijing Bu +3 more
doaj +1 more source
SREFBN: Enhanced feature block network for single‐image super‐resolution
IET Image Processing, 2022 Deep learning has assisted the field of single‐image super‐resolution (SR) in achieving new heights. However, the task of restoring a high‐resolution (HR) image from a highly degraded low‐resolution (LR) image is sophisticated due to poor image ...
Vachiraporn Ketsoi +3 more
doaj +1 more source