Papers Image Deconvolution
“Image Deconvolution” 태그가 달린 논문 78편 · 필터 해제
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse p…
DenoisingImage DeconvolutionReconstructing the Image Scanning Microscopy Dataset: an Inverse Problem
Confocal laser-scanning microscopy (CLSM) is one of the most popular optical architectures for fluorescence imaging. In CLSM, a focused laser beam excites the fluorescence emission from a specific specimen position. Some…
Image DeconvolutionGalaxy 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. This ill-posed linear inverse problem is usually solv…
DenoisingImage DeconvolutionRolling Shutter CorrectionDELAD: 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. Instead of using large over-parameterised generative networks to create sharp picture …
BenchmarkingBlind Image DeblurringDeblurringImage Deblurring+2Optimization-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. However, previous ODL approaches regard the traini…
Image DeconvolutionPoissonian 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. Among these cases, Poissonian noise is one of the most famous noises that appear in medical images and images take…
AstronomyImage DeconvolutionNonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network
Nonblind image deconvolution (NID) is about restoring the latent image with sharp details from a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep learning approach for NID using untr…
Bayesian InferenceImage DeconvolutionBlind Image Deconvolution Using Variational Deep Image Prior
Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize well …
Image DeconvolutionWiener Guided DIP for Unsupervised Blind Image Deconvolution
Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. The ill-posed nature of the problem requires adequate priors to arrive to a desirable solution. Recently, it has…
AstronomyImage DeconvolutionImage GenerationImplicit Neural Representations for Deconvolving SAS Images
Synthetic aperture sonar (SAS) image resolution is constrained by waveform bandwidth and array geometry. Specifically, the waveform bandwidth determines a point spread function (PSF) that blurs the locations of point sca…
Image DeconvolutionDeepRLS: 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. Motivated by the computatio…
Computational EfficiencyImage DeconvolutionLearning Discriminative Shrinkage Deep Networks for Image Deconvolution
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 imag…
Image DeconvolutionImage RestorationPlug-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.…
Image DeconvolutionCompressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks
The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide sta…
Compressive SensingImage DeconvolutionImage RestorationPoisson 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. The efficiency of t…
DenoisingImage DeconvolutionBlind 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. Since the problem is ill-posed, an image prior plays a significant …
Image DeconvolutionImage 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. We only require that the forward model be available and t…
DenoisingImage DeconvolutionDeep Learning for Handling Kernel/model Uncertainty in Image Deconvolution
Most existing non-blind image deconvolution methods assume that the given blurring kernel is error-free. In practice, blurring kernel often is estimated via some blind deblurring algorithm which is not exactly the truth.…
DeblurringDeep LearningImage DeconvolutionDeep Blind Video Super-resolution
Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration. However, this assumption does not hold for v…
Image DeconvolutionImage RestorationMotion EstimationSuper-Resolution+1Microscopy Image Restoration with Deep Wiener-Kolmogorov filters
Microscopy is a powerful visualization tool in biology, enabling the study of cells, tissues, and the fundamental biological processes; yet, the observed images typically suffer from blur and background noise. In this wo…
DeblurringDeep LearningDenoisingImage Deblurring+3