Papers Image Deconvolution
“Image Deconvolution” 태그가 달린 논문 78편 · 필터 해제
Joint multiband deconvolution for Euclid and Vera C. Rubin images
With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any singl…
DenoisingImage DeconvolutionSuper Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach
This work aims to reconstruct image sequences with Total Variation regularity in super-resolution. We consider, in particular, images of scenes for which the point-to-point image transformation is a plane projective tran…
Image DeconvolutionOptical Flow EstimationSuper-ResolutionDeep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects
As the density of spacecraft in Earth's orbit increases, their recognition, pose and trajectory identification becomes crucial for averting potential collisions and executing debris removal operations. However, training …
Image DeconvolutionImage ReconstructionImage RestorationPose Estimation+1The Empirical Watershed Wavelet
The empirical wavelet transform is an adaptive multiresolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain. However, existing 2D extensions are constrained by the …
Image DeconvolutionBlind Image Deconvolution by Generative-based Kernel Prior and Initializer via Latent Encoding
Blind image deconvolution (BID) is a classic yet challenging problem in the field of image processing. Recent advances in deep image prior (DIP) have motivated a series of DIP-based approaches, demonstrating remarkable s…
Generative Adversarial NetworkImage DeconvolutionLearning Point Spread Function Invertibility Assessment for Image Deconvolution
Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the …
Image DeconvolutionSolar multi-object multi-frame blind deconvolution with a spatially variant convolution neural emulator
The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post-facto image correction, essential for correcting the…
Image DeconvolutionGround-based image deconvolution with Swin Transformer UNet
As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the sp…
Computational EfficiencyImage DeconvolutionWhiteness-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. Compared to supervised and sem…
Bilevel OptimizationImage DeconvolutionPI-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. However, deconvolution presents a classical and challenging inverse computation problem…
DecoderImage DeconvolutionLearning 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. Here we employ a wavefront-cod…
Generative Adversarial NetworkImage DeconvolutionImage ReconstructionImage RestorationDeep, convergent, unrolled half-quadratic splitting for image deconvolution
In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-base…
DeblurringImage DeblurringImage DeconvolutionEchoes 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. Significant barriers to such analysis are the non-trivial noise properties of real astronom…
Image DeconvolutionVDIP-TGV: Blind Image Deconvolution via Variational Deep Image Prior Empowered by Total Generalized Variation
Recovering clear images from blurry ones with an unknown blur kernel is a challenging problem. Deep image prior (DIP) proposes to use the deep network as a regularizer for a single image rather than as a supervised model…
DeblurringImage DeconvolutionThe 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. In photon-limited conditions, however, the excessive amount of shot noise makes tradition…
Image DeconvolutionAccelerated Bayesian imaging by relaxed proximal-point Langevin sampling
This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to perform Bayesian inference in imaging inverse problems with an underlying convex geometry. The proposed strategy takes the form of a …
Bayesian InferenceImage DeconvolutionSelf-Supervised Single-Image Deconvolution with Siamese Neural Networks
Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful parameter selection for an optimal trade-…
Image DeconvolutionImage ReconstructionDeep learning-based deconvolution for interferometric radio transient reconstruction
Radio astronomy is currently thriving with new large ground-based radio telescopes coming online in preparation for the upcoming Square Kilometre Array (SKA). Facilities like LOFAR, MeerKAT/SKA, ASKAP/SKA, and the future…
AstronomyDeep LearningDenoisingImage Deconvolution+1A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution
In recent literature there are plenty of works that combine handcrafted and learnable regularizers to solve inverse imaging problems. While this hybrid approach has demonstrated promising results, the motivation for comb…
Computational EfficiencyDenoisingImage DeconvolutionNon-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. We focus on performing this task via…
Bayesian InferenceImage Deconvolution