Papers Image Deconvolution
“Image Deconvolution” 태그가 달린 논문 78편 · 필터 해제
Image Deconvolution with Deep Image and Kernel Priors
Image deconvolution is the process of recovering convolutional degraded images, which is always a hard inverse problem because of its mathematically ill-posed property. On the success of the recently proposed deep image …
DenoisingImage DeconvolutionSuper-ResolutionCycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry
Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimiz…
Generative Adversarial NetworkImage DeconvolutionBlind Image Deconvolution using Pretrained Generative Priors
This paper proposes a novel approach to regularize the ill-posed blind image deconvolution (blind image deblurring) problem using deep generative networks. We employ two separate deep generative models - one trained to p…
Blind Image DeblurringDeblurringImage DeblurringImage DeconvolutionDouglas-Rachford Networks: Learning Both the Image Prior and Data Fidelity Terms for Blind Image Deconvolution
Blind deconvolution problems are heavily ill-posed where the specific blurring kernel is not known. Recovering these images typically requires estimates of the kernel. In this paper, we present a method called Dr-Net, wh…
Image DeconvolutionThree dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
Due to image blurring image deconvolution is often used for studying biological structures in fluorescence microscopy. Fluorescence microscopy image volumes inherently suffer from intensity inhomogeneity, blur, and are c…
DenoisingImage DeconvolutionA Deep Optimization Approach for Image Deconvolution
In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be instable, …
Image DeconvolutionEdge-Based Blur Kernel Estimation Using Sparse Representation and Self-Similarity
Blind image deconvolution is the problem of recovering the latent image from the only observed blurry image when the blur kernel is unknown. In this paper, we propose an edge-based blur kernel estimation method for blind…
DeblurringImage DeconvolutionMPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution
Total variation (TV) regularization has proven effective for a range of computer vision tasks through its preferential weighting of sharp image edges. Existing TV-based methods, however, often suffer from the over-smooth…
Image DeconvolutionIterative Residual Image Deconvolution
Image deblurring, a.k.a. image deconvolution, recovers a clear image from pixel superposition caused by blur degradation. Few deep convolutional neural networks (CNN) succeed in addressing this task. In this paper, we fi…
DeblurringImage DeblurringImage DeconvolutionSimultaneous Fidelity and Regularization Learning for Image Restoration
Most existing non-blind restoration methods are based on the assumption that a precise degradation model is known. As the degradation process can only be partially known or inaccurately modeled, images may not be well re…
DenoisingImage DeconvolutionImage RestorationLearning Deep Gradient Descent Optimization for Image Deconvolution
As an integral component of blind image deblurring, non-blind deconvolution removes image blur with a given blur kernel, which is essential but difficult due to the ill-posed nature of the inverse problem. The predominan…
Blind Image DeblurringDeblurringImage DeblurringImage DeconvolutionBlind Image Deconvolution using Deep Generative Priors
This paper proposes a novel approach to regularize the \textit{ill-posed} and \textit{non-linear} blind image deconvolution (blind deblurring) using deep generative networks as priors. We employ two separate generative m…
DeblurringImage DeblurringImage DeconvolutionProperties on n-dimensional convolution for image deconvolution
Convolution system is linear and time invariant, and can describe the optical imaging process. Based on convolution system, many deconvolution techniques have been developed for optical image analysis, such as boosting t…
DenoisingGPUImage DeconvolutionImage Denoising+1Learning to Push the Limits of Efficient FFT-Based Image Deconvolution
This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based t…
Image DeconvolutionLearning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks. A remain…
DemosaickingDenoisingImage DeconvolutionMicroscopic Muscle Image Enhancement
We propose a robust image enhancement algorithm dedicated for muscle fiber specimen images captured by optical microscopes. Blur or out of focus problems are prevalent in muscle images during the image acquisition stage.…
DeblurringImage DeconvolutionImage EnhancementDirectional Mean Curvature for Textured Image Demixing
Approximation theory plays an important role in image processing, especially image deconvolution and decomposition. For piecewise smooth images, there are many methods that have been developed over the past thirty years.…
Image DeconvolutionCrowd Counting by Adapting Convolutional Neural Networks with Side Information
Computer vision tasks often have side information available that is helpful to solve the task. For example, for crowd counting, the camera perspective (e.g., camera angle and height) gives a clue about the appearance and…
Crowd CountingImage DeconvolutionLearning Fully Convolutional Networks for Iterative Non-blind Deconvolution
In this paper, we propose a fully convolutional networks for iterative non-blind deconvolution We decompose the non-blind deconvolution problem into image denoising and image deconvolution. We train a FCNN to remove nois…
DenoisingImage DeconvolutionImage DenoisingGuided Filter based Edge-preserving Image Non-blind Deconvolution
In this work, we propose a new approach for efficient edge-preserving image deconvolution. Our algorithm is based on a novel type of explicit image filter - guided filter. The guided filter can be used as an edge-preserv…
DeblurringDenoisingImage Deconvolution