126,671 research outputs found
Deep Burst Denoising
Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease noise variance. However, there are two downsides of long
exposures: (a) bright regions can exceed the sensor range, and (b) camera and
scene motion will result in blurred images. Another way of gathering more light
is to capture multiple short (thus noisy) frames in a "burst" and intelligently
integrate the content, thus avoiding the above downsides. In this paper, we use
the burst-capture strategy and implement the intelligent integration via a
recurrent fully convolutional deep neural net (CNN). We build our novel,
multiframe architecture to be a simple addition to any single frame denoising
model, and design to handle an arbitrary number of noisy input frames. We show
that it achieves state of the art denoising results on our burst dataset,
improving on the best published multi-frame techniques, such as VBM4D and
FlexISP. Finally, we explore other applications of image enhancement by
integrating content from multiple frames and demonstrate that our DNN
architecture generalizes well to image super-resolution
Incremental construction of LSTM recurrent neural network
Long Short--Term Memory (LSTM) is a recurrent neural network that
uses structures called memory blocks to allow the net remember
significant events distant in the past input sequence in order to
solve long time lag tasks, where other RNN approaches fail.
Throughout this work we have performed experiments using LSTM
networks extended with growing abilities, which we call GLSTM.
Four methods of training growing LSTM has been compared. These
methods include cascade and fully connected hidden layers as well
as two different levels of freezing previous weights in the
cascade case. GLSTM has been applied to a forecasting problem in a biomedical domain, where the input/output behavior of five
controllers of the Central Nervous System control has to be
modelled. We have compared growing LSTM results against other
neural networks approaches, and our work applying conventional
LSTM to the task at hand.Postprint (published version
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
IEEE Access special section editorial: Artificial intelligence enabled networking
With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)
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