193 research outputs found
Image Denoising with Graph-Convolutional Neural Networks
Recovering an image from a noisy observation is a key problem in signal
processing. Recently, it has been shown that data-driven approaches employing
convolutional neural networks can outperform classical model-based techniques,
because they can capture more powerful and discriminative features. However,
since these methods are based on convolutional operations, they are only
capable of exploiting local similarities without taking into account non-local
self-similarities. In this paper we propose a convolutional neural network that
employs graph-convolutional layers in order to exploit both local and non-local
similarities. The graph-convolutional layers dynamically construct
neighborhoods in the feature space to detect latent correlations in the feature
maps produced by the hidden layers. The experimental results show that the
proposed architecture outperforms classical convolutional neural networks for
the denoising task.Comment: IEEE International Conference on Image Processing (ICIP) 201
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene from
multiple unregistered low-resolution images have recently gained attention
thanks to a challenge proposed by the European Space Agency. This paper
presents an evolution of the winner of the challenge, showing how incorporating
non-local information in a convolutional neural network allows to exploit
self-similar patterns that provide enhanced regularization of the
super-resolution problem. Experiments on the dataset of the challenge show
improved performance over the state-of-the-art, which does not exploit
non-local information.Comment: arXiv admin note: text overlap with arXiv:1907.0649
Implementing graph neural networks with TensorFlow-Keras
Graph neural networks are a versatile machine learning architecture that
received a lot of attention recently. In this technical report, we present an
implementation of convolution and pooling layers for TensorFlow-Keras models,
which allows a seamless and flexible integration into standard Keras layers to
set up graph models in a functional way. This implies the usage of mini-batches
as the first tensor dimension, which can be realized via the new RaggedTensor
class of TensorFlow best suited for graphs. We developed the Keras Graph
Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras
that provides a set of Keras layers for graph networks which focus on a
transparent tensor structure passed between layers and an ease-of-use mindset
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