Results 31 to 40 of about 20,562 (267)
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 ...
Libin Sun +3 more
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IEEE Access, 2021 With the development of computational photography, single-lens camera combined with corresponding image deblurring algorithm is gradually becoming a new research direction, replacing complex modern optical imaging system such as single lens reflex (SLR ...
Dazhi Zhan +4 more
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Combining Motion Compensation with Spatiotemporal Constraint for Video Deblurring
Sensors, 2018 We propose a video deblurring method by combining motion compensation with spatiotemporal constraint for restoring blurry video caused by camera shake.
Jing Li, Weiguo Gong, Weihong Li
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Blur kernel estimation using normalized color-line priors [PDF]
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 This paper proposes a single-image blur kernel estimation algorithm that utilizes the normalized color-line prior to restore sharp edges without altering edge structures or enhancing noise. The proposed prior is derived from the color-line model, which has been successfully applied to non-blind deconvolution and many computer vision problems.
Wei-Sheng Lai +3 more
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Understanding Kernel Size in Blind Deconvolution
, 2019 Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results.
Ren, Dongwei, Si-Yao, Li, Yin, Qian
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Joint Blind Motion Deblurring and Depth Estimation of Light Field
, 2018 Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion.
A Beck +12 more
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Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
, 2020 Human visual system relies on both binocular stereo cues and monocular focusness cues to gain effective 3D perception. In computer vision, the two problems are traditionally solved in separate tracks.
A Newell +10 more
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Fast and easy blind deblurring using an inverse filter and PROBE
, 2017 PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring. Using the elementary modified inverse filter at its core, PROBE's experimental performance meets or exceeds the state of the art, both visually and quantitatively.
J Kotera +11 more
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Deep Self-Learning Network for Adaptive Pansharpening
Remote Sensing, 2019 Deep learning (DL)-based paradigms have recently made many advances in image pansharpening. However, most of the existing methods directly downscale the multispectral (MSI) and panchromatic (PAN) images with default blur kernel to construct the training ...
Jie Hu, Zhi He, Jiemin Wu
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DETECTING AND CORRECTING MOTION BLUR FROM IMAGES SHOT WITH CHANNEL-DEPENDENT EXPOSURE TIME [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 This article describes a pipeline developed to automatically detect and correct motion blur due to the airplane motion in aerial images provided by a digital camera system with channel-dependent exposure times.
L. Lelégard +3 more
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