Image Deconvolution
21 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Image Deconvolution
Stained mice brain blood vessels. Confocal-LFM
Most implemented papers
Image Deconvolution via Noise-Tolerant Self-Supervised Inversion
We propose a general framework for solving inverse problems in the presence of noise that requires no signal prior, no noise estimate, and no clean training data.
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.
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.
Wiener Guided DIP for Unsupervised Blind Image Deconvolution
In addition, the image generator reproduces low-frequency features of the deconvolved image faster than that of a blurry image.
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.
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.
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.
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.
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.
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.
