Results 11 to 20 of about 2,318 (187)
Stereoscopic video deblurring transformer
Scientific ReportsStereoscopic cameras, such as those in mobile phones and various recent intelligent systems, are becoming increasingly common. Multiple variables can impact the stereo video quality, e.g., blur distortion due to camera/object movement.
Hassan Imani +3 more
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Motion Deblurring of Faces [PDF]
International Journal of Computer Vision, 2018 Face analysis is a core part of computer vision, in which remarkable progress has been observed in the past decades. Current methods achieve recognition and tracking with invariance to fundamental modes of variation such as illumination, 3D pose, expressions.
Grigorios G. Chrysos +2 more
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Direct Sparse Deblurring [PDF]
Journal of Mathematical Imaging and Vision, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yifei Lou +2 more
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Deblurring gaussian blur [PDF]
Computer Vision, Graphics, and Image Processing, 1986 Summary: Gaussian blur, or convolution against a Gaussian kernel, is a common model for image and signal degradation. In general, the process of reversing Gaussian blur is unstable, and cannot be represented as a convolution filter in the spatial domain.
Robert A. Hummel +2 more
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Iterative Dual CNNs for Image Deblurring
Mathematics, 2022 Image deblurring attracts research attention in the field of image processing and computer vision. Traditional deblurring methods based on statistical prior largely depend on the selected prior type, which limits their restoring ability.
Jinbin Wang, Ziqi Wang, Aiping Yang
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CORE-Deblur: Parallel MRI Reconstruction by Deblurring using compressed sensing [PDF]
Magnetic Resonance Imaging, 2020 In this work we introduce a new method that combines Parallel MRI and Compressed Sensing (CS) for accelerated image reconstruction from subsampled k-space data. The method first computes a convolved image, which gives the convolution between a user-defined kernel and the unknown MR image, and then reconstructs the image by CS-based image deblurring, in
Shimron, E., Webb, A.G., Azhari, H.
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Infrared Image Deblurring Based on Generative Adversarial Networks
International Journal of Optics, 2021 Blind deblurring of a single infrared image is a challenging computer vision problem. Because the blur is not only caused by the motion of different objects but also by the relative motion and jitter of cameras, there is a change of scene depth.
Yuqing Zhao +4 more
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Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
Applied Sciences, 2023 Most existing image deblurring methods are based on the estimation of blur kernels and end-to-end learning of the mapping relationship between blurred and sharp images.
Zihe Zhao +4 more
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IEEE Transactions on Multimedia, 2022 Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results.
Chih-Hung Liang +3 more
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Joint Face Super-Resolution and Deblurring Using Generative Adversarial Network
IEEE Access, 2020 Facial image super-resolution (SR) is an important aspect of facial analysis, and it can contribute significantly to tasks such as face alignment, face recognition, and image-based 3D reconstruction. Recent convolutional neural network (CNN) based models
Jung Un Yun, Byungho Jo, In Kyu Park
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