Results 21 to 30 of about 20,368 (248)
Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution
Information, 2023 Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan +2 more
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IET Intelligent Transport Systems, 2023 Maritime surveillance systems have been commonly exploited in vessel traffic services. The maritime visual information can be obtained through shore‐borne, ship‐borne or air‐borne cameras. However, the obtained visual data often suffers from blur effects
Yanhong Huang +2 more
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Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal [PDF]
, 2015 In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional ...
Cao, Wenfei +3 more
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Adaptive Optics Image Restoration via Regularization Priors With Gaussian Statistics
IEEE Access, 2020 In order to compensate for any failure on the use of point spread function (blur kernel) estimation and image estimation priors, we propose a novel regularization priors scheme with adapting the parameter for image restoration involving adaptive optics ...
Dongming Li, Guangjie Qiu, Lijuan Zhang
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Concurrent Video Denoising and Deblurring for Dynamic Scenes
IEEE Access, 2021 Dynamic scene video deblurring is a challenging task due to the spatially variant blur inflicted by independently moving objects and camera shakes. Recent deep learning works bypass the ill-posedness of explicitly deriving the blur kernel by learning ...
Efklidis Katsaros +3 more
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Blur-Kernel Estimation from Spectral Irregularities [PDF]
, 2012 We describe a new method for recovering the blur kernel in motion-blurred images based on statistical irregularities their power spectrum exhibits. This is achieved by a power-law that refines the one traditionally used for describing natural images.
Amit Goldstein, Raanan Fattal
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A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
Applied Sciences, 2022 Aiming at the motion blur restoration of large-scale dual-channel space-variant images, this paper proposes a dual-channel image deblurring method based on the idea of block aggregation, by studying imaging principles and existing algorithms.
Yang Bai +4 more
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Blur kernel estimation of noisy-blurred image via dynamic structure prior
Neurocomputing, 2020 Abstract An accurate blur kernel is key to blind image deblurring and kernel estimation heavily relies on strong edges in the observed image [ 1 , 2, 3]. Previous methods [4] [5] leveraging image gradient prior with i.i.d statistics can hardly restrict strong edges in a noisy-blurred image, since both noise and strong edges are presented as strong ...
Xueling Chen +4 more
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A motion parameters estimating method based on deep learning for visual blurred object tracking
IET Image Processing, 2021 Tracking the specific object in the blurred scenes is one of the challenging problems in computer vision and image processing. The accuracy and performance of trackers within the blur frames usually demonstrate a severe decrease.
Iman Iraei, Karim Faez
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Edge-based blur kernel estimation using patch priors [PDF]
IEEE International Conference on Computational Photography (ICCP), 2013 Blind image deconvolution, i.e., estimating a blur kernel k and a latent image x from an input blurred image y, is a severely ill-posed problem. In this paper we introduce a new patch-based strategy for kernel estimation in blind deconvolution. Our approach estimates a “trusted” subset of x by imposing a patch prior specifically tailored towards ...
null Libin Sun +3 more
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