Results 11 to 20 of about 9,140,641 (221)
Deep Image Deblurring: A Survey [PDF]
International Journal of Computer Vision, 2022 Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published
Kaihao Zhang +6 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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Zero-shot realistic image deblurring with consistency model
Complex & Intelligent SystemsAt present, diffusion-based image deblurring methods rely on paired blurry-clear datasets for training, and the types of blur causes in image synthesis cannot yet be determined with sufficient precision to model real-world scene blur datasets ...
Zhaohan Wang +2 more
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Recent Progress in Image Deblurring
CoRR, 2014 This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to ...
Ruxin Wang 0002, Dacheng Tao
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Stereoscopic video deblurring transformer [PDF]
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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Symmetrization techniques in image deblurring
ETNA - Electronic Transactions on Numerical Analysis, 2023 This paper presents a couple of preconditioning techniques that can be used to enhance the performance of iterative regularization methods applied to image deblurring problems with a variety of point spread functions (PSFs) and boundary conditions. More precisely, we first consider the anti-identity preconditioner, which symmetrizes the coefficient ...
Donatelli M., Ferrari P., Gazzola S.
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Reference-Based Multi-Level Features Fusion Deblurring Network for Optical Remote Sensing Images
Remote Sensing, 2022 Blind image deblurring is a long-standing challenge in remote sensing image restoration tasks. It aims to recover a latent sharp image from a blurry image while the blur kernel is unknown.
Zhiyuan Li +4 more
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Convolutional Deblurring for Natural Imaging [PDF]
IEEE Transactions on Image Processing, 2020 In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections.
Mahdi S. Hosseini +1 more
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Two-Level Wavelet-Based Convolutional Neural Network for Image Deblurring
IEEE Access, 2021 Image deblurring aims to restore the latent sharp image from the blurred one. In recent years, some learning-based image deblurring methods have achieved significant advances.
Yeyun Wu, Pan Qian, Xiaofeng Zhang
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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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