26,342 research outputs found
Task-Driven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned
dictionary has been the focus of much recent research in machine learning,
neuroscience and signal processing. For signals such as natural images that
admit such sparse representations, it is now well established that these models
are well suited to restoration tasks. In this context, learning the dictionary
amounts to solving a large-scale matrix factorization problem, which can be
done efficiently with classical optimization tools. The same approach has also
been used for learning features from data for other purposes, e.g., image
classification, but tuning the dictionary in a supervised way for these tasks
has proven to be more difficult. In this paper, we present a general
formulation for supervised dictionary learning adapted to a wide variety of
tasks, and present an efficient algorithm for solving the corresponding
optimization problem. Experiments on handwritten digit classification, digital
art identification, nonlinear inverse image problems, and compressed sensing
demonstrate that our approach is effective in large-scale settings, and is well
suited to supervised and semi-supervised classification, as well as regression
tasks for data that admit sparse representations.Comment: final draft post-refereein
A GCV based Arnoldi-Tikhonov regularization method
For the solution of linear discrete ill-posed problems, in this paper we
consider the Arnoldi-Tikhonov method coupled with the Generalized Cross
Validation for the computation of the regularization parameter at each
iteration. We study the convergence behavior of the Arnoldi method and its
properties for the approximation of the (generalized) singular values, under
the hypothesis that Picard condition is satisfied. Numerical experiments on
classical test problems and on image restoration are presented
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Conventionally, image denoising and high-level vision tasks are handled
separately in computer vision. In this paper, we cope with the two jointly and
explore the mutual influence between them. First we propose a convolutional
neural network for image denoising which achieves the state-of-the-art
performance. Second we propose a deep neural network solution that cascades two
modules for image denoising and various high-level tasks, respectively, and use
the joint loss for updating only the denoising network via back-propagation. We
demonstrate that on one hand, the proposed denoiser has the generality to
overcome the performance degradation of different high-level vision tasks. On
the other hand, with the guidance of high-level vision information, the
denoising network can generate more visually appealing results. To the best of
our knowledge, this is the first work investigating the benefit of exploiting
image semantics simultaneously for image denoising and high-level vision tasks
via deep learning. The code is available online
https://github.com/Ding-Liu/DeepDenoising.Comment: the 27th International Joint Conference on Artificial Intelligence
(2018
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Recent developments in deep learning have revolutionized the paradigm of
image restoration. However, its applications on real image denoising are still
limited, due to its sensitivity to training data and the complex nature of real
image noise. In this work, we combine the robustness merit of model-based
approaches and the learning power of data-driven approaches for real image
denoising. Specifically, by integrating graph Laplacian regularization as a
trainable module into a deep learning framework, we are less susceptible to
overfitting than pure CNN-based approaches, achieving higher robustness to
small datasets and cross-domain denoising. First, a sparse neighborhood graph
is built from the output of a convolutional neural network (CNN). Then the
image is restored by solving an unconstrained quadratic programming problem,
using a corresponding graph Laplacian regularizer as a prior term. The proposed
restoration pipeline is fully differentiable and hence can be end-to-end
trained. Experimental results demonstrate that our work is less prone to
overfitting given small training data. It is also endowed with strong
cross-domain generalization power, outperforming the state-of-the-art
approaches by a remarkable margin
A proximal iteration for deconvolving Poisson noisy images using sparse representations
We propose an image deconvolution algorithm when the data is contaminated by
Poisson noise. The image to restore is assumed to be sparsely represented in a
dictionary of waveforms such as the wavelet or curvelet transforms. Our key
contributions are: First, we handle the Poisson noise properly by using the
Anscombe variance stabilizing transform leading to a {\it non-linear}
degradation equation with additive Gaussian noise. Second, the deconvolution
problem is formulated as the minimization of a convex functional with a
data-fidelity term reflecting the noise properties, and a non-smooth
sparsity-promoting penalties over the image representation coefficients (e.g.
-norm). Third, a fast iterative backward-forward splitting algorithm is
proposed to solve the minimization problem. We derive existence and uniqueness
conditions of the solution, and establish convergence of the iterative
algorithm. Finally, a GCV-based model selection procedure is proposed to
objectively select the regularization parameter. Experimental results are
carried out to show the striking benefits gained from taking into account the
Poisson statistics of the noise. These results also suggest that using
sparse-domain regularization may be tractable in many deconvolution
applications with Poisson noise such as astronomy and microscopy
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