Results 41 to 50 of about 36,154 (265)
Blur2Sharp: A GAN-Based Model for Document Image Deblurring
International Journal of Computational Intelligence Systems, 2021 The advances in mobile technology and portable cameras have facilitated enormously the acquisition of text images. However, the blur caused by camera shake or out-of-focus problems may affect the quality of acquired images and their use as input for ...
Hala Neji +4 more
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Blind Image Deblurring via Reweighted Graph Total Variation
, 2017 Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry
Bai, Yuanchao +3 more
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Degraded reality: Using VR/AR to simulate visual impairments [PDF]
, 2018 The effects of eye disease cannot be depicted accurately using traditional media. Consequently, public understanding of eye disease is often poor. We present a VR/AR system for simulating common visual impairments, including disability glare, spatial ...
Jones, P. R., Ometto, G.
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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 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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Mathematical Problems of Computer Science, 2021 In this paper, we present deep learning-based blind image deblurring methods for estimating and removing a non-uniform motion blur from a single blurry image. We propose two fully convolutional neural networks (CNN) for solving the problem.
Misak T. Shoyan +2 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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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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Mathematical Degradation Model Learning for Terahertz Image Super-Resolution
IEEE Access, 2021 This study proposes a super-resolution (SR) method for terahertz time-domain spectroscopy (THz-TDS) images, combining a convolutional neural network (CNN) and a mathematical degradation model.
Yao Lu, Qi Mao, Jingbo Liu
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