Results 21 to 30 of about 1,764,639 (264)
IEEE Access, 2020 Blind image deblurring, composed of estimating blur kernel and non-blind deconvolution, is an extremely ill-posed problem. However, previous deblurring methods still cannot solve delta kernel or noise problem well and avoid ringing artifacts in restored ...
Hongtian Zhao +3 more
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Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring
Sensors, 2017 Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process.
Naixue Xiong +5 more
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Image Blind Deblurring Using an Adaptive Patch Prior
Tsinghua Science and Technology, 2019 Image blind deblurring uses an estimated blur kernel to obtain an optimal restored original image with sharp features from a degraded image with blur and noise artifacts.
Yongde Guo, Hongbing Ma
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Robust Image Restoration for Motion Blur of Image Sensors
Sensors, 2016 Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly.
Fasheng Yang +4 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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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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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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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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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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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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