Variational Dirichlet Blur Kernel Estimation
IEEE Transactions on Image Processing, 2015 Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a
Xu, Zhou +4 more
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Convergence Analysis of MAP Based Blur Kernel Estimation [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their ...
Cho, Sunghyun, Lee, Seungyong
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Reconstruction of noisy and blurred images using blur kernel
IOP Conference Series: Materials Science and Engineering, 2017 Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to ...
Vijayan Ellappan, Vishal Chopra
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Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric [PDF]
Scientific ReportsSharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing.
Amit Pandey +4 more
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Adaptive Image Deblurring Convolutional Neural Network with Meta-Tuning [PDF]
SensorsMotion blur is a complex phenomenon caused by the relative movement between an observed object and an imaging sensor during the exposure time, resulting in degradation in the image quality.
Quoc-Thien Ho +3 more
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Multi-Regularization-Constrained Blur Kernel Estimation Method for Blind Motion Deblurring
IEEE Access, 2019 Blur kernel (BK) estimation is the crucial technique to guarantee the success of blind image deblurring. In this paper, we propose a multi-regularization-constrained method to estimate an accurate BK from a single motion-blurred image. First, in order to
Shu Tang +8 more
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Simultaneous Tumor Segmentation, Image Restoration, and Blur Kernel Estimation in PET Using Multiple Regularizations. [PDF]
Comput Vis Image Underst, 2017 Li L, Wang J, Lu W, Tan S.
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Learnable Blur Kernel for Single-Image Defocus Deblurring in the Wild [PDF]
AAAI Conference on Artificial Intelligence, 2022 Recent research showed that the dual-pixel sensor has made great progress in defocus map estimation and image defocus deblurring. However, extracting real-time dual-pixel views is troublesome and complex in algorithm deployment.
Jucai Zhai +4 more
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Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors
IEEE Access, 2023 The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models.
Zhe Li +3 more
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Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution
Information, 2023 Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version.
Asif Hussain Khan +2 more
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