Image Deconvolution
21 papers with code • 0 benchmarks • 1 datasets
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Stained mice brain blood vessels. Confocal-LFM
Latest papers
Deep, convergent, unrolled half-quadratic splitting for image deconvolution
6zhc/decun
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Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm.
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
mi2g/accelerated-langevin-imla
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This discretisation is asymptotically unbiased for Gaussian targets and shown to converge in an accelerated manner for any target that is $\kappa$-strongly log-concave (i. e., requiring in the order of $\sqrt{\kappa}$ iterations to converge, similarly to accelerated optimisation schemes), comparing favorably to [M. Pereyra, L. Vargas Mieles, K. C.
Deep learning-based deconvolution for interferometric radio transient reconstruction
Finally, based on the test data, we evaluate the source profile reconstruction performance of the proposed methods and classical image deconvolution algorithm CLEAN applied frame-by-frame.
Non-Log-Concave and Nonsmooth Sampling via Langevin Monte Carlo Algorithms
We study the problem of approximate sampling from non-log-concave distributions, e. g., Gaussian mixtures, which is often challenging even in low dimensions due to their multimodality.
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
xiuheng-wang/tuning_free_pnp_hsi_deconvolution
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Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices.
Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMM
Removing optical and atmospheric blur from galaxy images significantly improves galaxy shape measurements for weak gravitational lensing and galaxy evolution studies.
Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network
scut-mingqinchen/Model_Uncertainty_NID
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International Journal of Computer Vision 2022
Nonblind image deconvolution (NID) is about restoring the latent image with sharp details from a noisy blurred one using a known blur kernel.
Blind Image Deconvolution Using Variational Deep Image Prior
dong-huo/vdip-deconvolution
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Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization.
Learning Discriminative Shrinkage Deep Networks for Image Deconvolution
setsunil/dsdnet
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Most existing methods usually formulate the non-blind deconvolution problem into a maximum-a-posteriori framework and address it by manually designing kinds of regularization terms and data terms of the latent clear images.
Plug-and-Play Quantum Adaptive Denoiser for Deconvolving Poisson Noisy Images
A new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme is proposed in this paper, by embedding a recently introduced adaptive denoiser using the Schroedinger equation's solutions of quantum physics.
