Results 71 to 80 of about 388,321 (273)
Light Field Super-Resolution Via Graph-Based Regularization
, 2017 Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus, to depth estimation and image-based rendering ...
Frossard, Pascal, Rossi, Mattia
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Although most malignant germ cell tumors (GCTs) are highly curable with cisplatin‐based therapy, options for patients with multiply relapsed/refractory disease remain limited. For this population, we report the first pediatric use of gemcitabine, docetaxel, melphalan, and carboplatin (GemDMC) as part of sequential cycles of high‐dose ...
Maria Frost +10 more
wiley +1 more source
Multi‐feature fusion attention network for single image super‐resolution
IET Image Processing, 2023 Single Image Super‐Resolution algorithms have made enormous progress in recent years. However, many previous Convolution Neural Network (CNN) based Super‐Resolution algorithms only stack uniform convolution layers of fixed kernel size, and frequently ...
Jiacheng Chen +3 more
doaj +1 more source
Single-Image Super-Resolution: A Benchmark [PDF]
, 2014 Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics.
Chih-Yuan Yang, Chao Ma, Ming-Hsuan Yang
openaire +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background L‐asparaginase is a critical component in treatment protocols for pediatric acute lymphoblastic leukemia. Acute pancreatitis reactions can necessitate delays and, in some cases, discontinuation of L‐asparaginase, which compromises outcomes.
Edward J. Raack +39 more
wiley +1 more source
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
, 2017 Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images from one single area in the low resolution (LR) input, which limits the ...
B Cai +9 more
core +1 more source
Super-resolution single-photon imaging at 8.2 kilometers
Optics Express, 2020 Single-photon light detection and ranging (LiDAR), offering single-photon sensitivity and picosecond time resolution, has been widely adopted for active imaging applications. Long-range active imaging is a great challenge, because the spatial resolution degrades significantly with the imaging range due to the diffraction limit of the optics, and only ...
Zheng-Ping Li +8 more
openaire +3 more sources
Organoids in pediatric cancer research
FEBS Letters, EarlyView.Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley +1 more source
Robust Prior-Based Single Image Super Resolution Under Multiple Gaussian Degradations
IEEE Access, 2020 Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance.
Wenyi Wang +4 more
doaj +1 more source
FEBS Letters, EarlyView.Fluorescent probes allow dynamic visualization of phosphoinositides in living cells (left), whereas mass spectrometry provides high‐sensitivity, isomer‐resolved quantitation (right). Their synergistic use captures complementary aspects of lipid signaling. This review illustrates how these approaches reveal the spatiotemporal regulation and quantitative
Hiroaki Kajiho +3 more
wiley +1 more source