13,917 research outputs found
Image recovery using a new nonlinear adaptive filter based on neural networks
This work defines a new nonlinear adaptive filter based on a feed-forward neural network with the capacity of significantly reducing the additive noise of an image. Even though measurements have been carried out using x-ray images with additive white Gaussian noise, it is possible to extend the results to other type of images. Comparisons have been carried out with the Weiner filter because it is the most effective option for reducing Gaussian noise. In most of the cases, image reconstruction using the proposed method has produced satisfactory results. Finally, some conclusions and future work lines are presented.Instituto de Investigación en Informátic
Image recovery using a new nonlinear adaptive filter based on neural networks
This work defines a new nonlinear adaptive filter based on a feed-forward neural network with the capacity of significantly reducing the additive noise of an image. Even though measurements have been carried out using x-ray images with additive white Gaussian noise, it is possible to extend the results to other type of images. Comparisons have been carried out with the Weiner filter because it is the most effective option for reducing Gaussian noise. In most of the cases, image reconstruction using the proposed method has produced satisfactory results. Finally, some conclusions and future work lines are presented.Instituto de Investigación en Informátic
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
In this paper we develop a novel computational sensing framework for sensing
and recovering structured signals. When trained on a set of representative
signals, our framework learns to take undersampled measurements and recover
signals from them using a deep convolutional neural network. In other words, it
learns a transformation from the original signals to a near-optimal number of
undersampled measurements and the inverse transformation from measurements to
signals. This is in contrast to traditional compressive sensing (CS) systems
that use random linear measurements and convex optimization or iterative
algorithms for signal recovery. We compare our new framework with
-minimization from the phase transition point of view and demonstrate
that it outperforms -minimization in the regions of phase transition
plot where -minimization cannot recover the exact solution. In
addition, we experimentally demonstrate how learning measurements enhances the
overall recovery performance, speeds up training of recovery framework, and
leads to having fewer parameters to learn
Learning a Dilated Residual Network for SAR Image Despeckling
In this paper, to break the limit of the traditional linear models for
synthetic aperture radar (SAR) image despeckling, we propose a novel deep
learning approach by learning a non-linear end-to-end mapping between the noisy
and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is
based on dilated convolutions, which can both enlarge the receptive field and
maintain the filter size and layer depth with a lightweight structure. In
addition, skip connections and residual learning strategy are added to the
despeckling model to maintain the image details and reduce the vanishing
gradient problem. Compared with the traditional despeckling methods, the
proposed method shows superior performance over the state-of-the-art methods on
both quantitative and visual assessments, especially for strong speckle noise.Comment: 18 pages, 13 figures, 7 table
- …