Image Deconvolution
22 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
Latest papers with no code
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization.
Poissonian Blurred Image Deconvolution by Framelet based Local Minimal Prior
Image production tools do not always create a clear image, noisy and blurry images are sometimes created.
Implicit Neural Representations for Deconvolving SAS Images
This work is an important first step towards applying neural networks for SAS image deconvolution.
DeepRLS: A Recurrent Network Architecture with Least Squares Implicit Layers for Non-blind Image Deconvolution
In this work, we study the problem of non-blind image deconvolution and propose a novel recurrent network architecture that leads to very competitive restoration results of high image quality.
Herschel and Odin observations of H2O, CO, CH, CH+, and NII in the barred spiral galaxy NGC 1365. Bar-induced activity in the outer and inner circumnuclear tori
Herschel has observed the central region of NGC 1365 in two positions, and both its SPIRE and PACS observations are available in the Herschel Science Archive.
Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks
However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected.
Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme
This paper introduces a new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme based on a recently proposed denoiser using the Schroedinger equation's solutions of quantum physics.
Blind Image Deconvolution using Student's-t Prior with Overlapping Group Sparsity
In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel.
Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution
It is known that non-blind deconvolution is susceptible to such a kernel/model error.
SPRING: A fast stochastic proximal alternating method for non-smooth non-convex optimization
We introduce SPRING, a novel stochastic proximal alternating linearized minimization algorithm for solving a class of non-smooth and non-convex optimization problems.
