380 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
A Deep Learning Approach to Structured Signal Recovery
In this paper, we develop a new framework for sensing and recovering
structured signals. In contrast to compressive sensing (CS) systems that employ
linear measurements, sparse representations, and computationally complex
convex/greedy algorithms, we introduce a deep learning framework that supports
both linear and mildly nonlinear measurements, that learns a structured
representation from training data, and that efficiently computes a signal
estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an
unsupervised feature learner. SDA enables us to capture statistical
dependencies between the different elements of certain signals and improve
signal recovery performance as compared to the CS approach
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
Nonlocal self-similarity within natural images has become an increasingly
popular prior in deep-learning models. Despite their successful image
restoration performance, such models remain largely uninterpretable due to
their black-box construction. Our previous studies have shown that
interpretable construction of a fully convolutional denoiser (CDLNet), with
performance on par with state-of-the-art black-box counterparts, is achievable
by unrolling a dictionary learning algorithm. In this manuscript, we seek an
interpretable construction of a convolutional network with a nonlocal
self-similarity prior that performs on par with black-box nonlocal models. We
show that such an architecture can be effectively achieved by upgrading the
sparsity prior of CDLNet to a weighted group-sparsity prior. From this
formulation, we propose a novel sliding-window nonlocal operation, enabled by
sparse array arithmetic. In addition to competitive performance with black-box
nonlocal DNNs, we demonstrate the proposed sliding-window sparse attention
enables inference speeds greater than an order of magnitude faster than its
competitors.Comment: 11 pages, 8 figures, 6 table
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