3,485 research outputs found
Wavelet/shearlet hybridized neural networks for biomedical image restoration
Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets
Towards real-time reconstruction of velocity fluctuations in turbulent channel flow
We develop a framework for efficient streaming reconstructions of turbulent
velocity fluctuations from limited sensor measurements with the goal of
enabling real-time applications. The reconstruction process is simplified by
computing linear estimators using flow statistics from an initial training
period and evaluating their performance during a subsequent testing period with
data obtained from direct numerical simulation. We address cases where (i) no,
(ii) limited, and (iii) full-field training data are available using estimators
based on (i) resolvent modes, (ii) resolvent-based estimation, and (iii)
spectral proper orthogonal decomposition modes. During training, we introduce
blockwise inversion to accurately and efficiently compute the resolvent
operator in an interpretable manner. During testing, we enable efficient
streaming reconstructions by using a temporal sliding discrete Fourier
transform to recursively update Fourier coefficients using incoming
measurements. We use this framework to reconstruct with minimal time delay the
turbulent velocity fluctuations in a minimal channel at from sparse planar measurements. We evaluate reconstruction accuracy in
the context of the extent of data required and thereby identify potential use
cases for each estimator. The reconstructions capture large portions of the
dynamics from relatively few measurement planes when the linear estimators are
computed with sufficient fidelity. We also evaluate the efficiency of our
reconstructions and show that the present framework has the potential to help
enable real-time reconstructions of turbulent velocity fluctuations in an
analogous experimental setting.Comment: 36 pages, 22 figures, accepted by Physical Review Fluid
Reconfigurable implementation of recursive DCT kernels for reduced quantization noise
Time multiplexed implementations of the recursive DCT
processors are widely used in many multimedia and compression applications. Recently proposed three Goertzel kernels offer significant improvement (up to 90 %) in the noise performance of the time-multiplexed architecture to allow word-length specifications get reduced. In this paper, a highly optimized reconfigurable DCT architecture is proposed that can perform the function of three different kemels (Type A, B and C) on Virtex FPG
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure
- …