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
Latest papers with no code
Whiteness-based bilevel learning of regularization parameters in imaging
We consider an unsupervised bilevel optimization strategy for learning regularization parameters in the context of imaging inverse problems in the presence of additive white Gaussian noise.
PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task.
Learning to See Through Dazzle
Machine vision is susceptible to laser dazzle, where intense laser light can blind and distort its perception of the environment through oversaturation or permanent damage to sensor pixels.
Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors
Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms.
VDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation
However, we empirically find that VDIP struggles with processing image details and tends to generate suboptimal results when the blur kernel is large.
The Secrets of Non-Blind Poisson Deconvolution
Non-blind image deconvolution has been studied for several decades but most of the existing work focuses on blur instead of noise.
Self-Supervised Single-Image Deconvolution with Siamese Neural Networks
Recently, self-supervised blind-spot neural networks were successfully adopted for image deconvolution by including a known point-spread function in the end-to-end training.
An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees
The proposed model is a first attempt to handle the classical HSI degradation problem with different blurring kernels and noise levels via a single deep equilibrium model with significant computational efficiency.
Reconstructing the Image Scanning Microscopy Dataset: an Inverse Problem
Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging.
DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior
We present a model for non-blind image deconvolution that incorporates the classic iterative method into a deep learning application.
