1,931 research outputs found
Scene-adapted plug-and-play algorithm with convergence guarantees
Recent frameworks, such as the so-called plug-and-play, allow us to leverage
the developments in image denoising to tackle other, and more involved,
problems in image processing. As the name suggests, state-of-the-art denoisers
are plugged into an iterative algorithm that alternates between a denoising
step and the inversion of the observation operator. While these tools offer
flexibility, the convergence of the resulting algorithm may be difficult to
analyse. In this paper, we plug a state-of-the-art denoiser, based on a
Gaussian mixture model, in the iterations of an alternating direction method of
multipliers and prove the algorithm is guaranteed to converge. Moreover, we
build upon the concept of scene-adapted priors where we learn a model targeted
to a specific scene being imaged, and apply the proposed method to address the
hyperspectral sharpening problem
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Plug-and-play (PnP) is a non-convex framework that integrates modern
denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or
other proximal algorithms. An advantage of PnP is that one can use pre-trained
denoisers when there is not sufficient data for end-to-end training. Although
PnP has been recently studied extensively with great empirical success,
theoretical analysis addressing even the most basic question of convergence has
been insufficient. In this paper, we theoretically establish convergence of
PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain
Lipschitz condition on the denoisers. We then propose real spectral
normalization, a technique for training deep learning-based denoisers to
satisfy the proposed Lipschitz condition. Finally, we present experimental
results validating the theory.Comment: Published in the International Conference on Machine Learning, 201
Regularized Fourier ptychography using an online plug-and-play algorithm
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, we propose a new online PnP algorithm for Fourier ptychographic microscopy (FPM) based on the accelerated proximal gradient method (APGM). Specifically, the proposed algorithm uses only a subset of measurements, which makes it scalable to a large set of measurements. We validate the algorithm by showing that it can lead to significant performance gains on both simulated and experimental data.https://arxiv.org/abs/1811.00120Published versio
Regularized Fourier ptychography using an online plug-and-play algorithm
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, we propose a new online PnP algorithm for Fourier ptychographic microscopy (FPM) based on the accelerated proximal gradient method (APGM). Specifically, the proposed algorithm uses only a subset of measurements, which makes it scalable to a large set of measurements. We validate the algorithm by showing that it can lead to significant performance gains on both simulated and experimental data.https://arxiv.org/abs/1811.00120Published versio
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image
reconstruction that specifies the prior through an image denoiser. While PnP
algorithms are well understood for denoisers performing maximum a posteriori
probability (MAP) estimation, they have not been analyzed for the minimum mean
squared error (MMSE) denoisers. This letter addresses this gap by establishing
the first theoretical convergence result for the iterative
shrinkage/thresholding algorithm (ISTA) variant of PnP for MMSE denoisers. We
show that the iterates produced by PnP-ISTA with an MMSE denoiser converge to a
stationary point of some global cost function. We validate our analysis on
sparse signal recovery in compressive sensing by comparing two types of
denoisers, namely the exact MMSE denoiser and the approximate MMSE denoiser
obtained by training a deep neural net
Deep Learning for Linear Inverse Problems Using the Plug-and-Play Priors Framework
Linear inverse problems appear in many applications, where different algorithms are typically employed to solve each inverse problem. Nowadays, the rapid development of deep learning (DL) provides a fresh perspective for solving the linear inverse problem: a number of well-designed network architectures results in state-of-the-art performance in many applications. In this overview paper, we present the combination of the DL and the Plug-and-Play priors (PPP) framework, showcasing how it allows solving various inverse problems by leveraging the impressive capabilities of existing DL based denoising algorithms. Open challenges and potential future directions along this line of research are also discussed
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