Results 41 to 50 of about 512,229 (281)
Classifying Discriminative Features for Blur Detection
, 2016 Blur detection in a single image is challenging especially when the blur is spatially-varying. Developing discriminative blur features is an open problem. In this paper, we propose a new kernel-specific feature vector consisting of the information of a
Pang, YW +4 more
core +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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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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
doaj +1 more source
A survey of kernel and spectral methods for clustering [PDF]
, 2007 Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods ...
Masulli, F. +11 more
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Advanced Materials, EarlyView.AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll +19 more
wiley +1 more source
Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution
IEEE Access, 2022 Blind super-resolution (blind-SR) is an important task in the field of computer vision and has various applications in real-world. Blur kernel estimation is the main element of blind-SR along with the adaptive SR networks and a more accurately estimated ...
Youngsoo Kim +3 more
doaj +1 more source
Mixture of Gaussian Blur Kernel Representation for Blind Image Restoration
, 2019 The use of blind image restoration, sharpness of edges may frequently be restored using previous information from a picture. De-blurring is the technique of taking out blurring flaws of the steady photographs, including motion or defocus aberration ...
Vimal , Vrince
core +1 more source