Results 11 to 20 of about 1,764,639 (264)
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 ...
Sunghyun Cho, Seungyong Lee 0001
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Blur kernel estimation approach to blind reverberation time estimation [PDF]
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 Reverberation time is an important parameter for characterizing acoustic environments. It is useful in many applications including acoustic scene analysis, robust automatic speech recognition and dereverberation. Given knowledge of the acoustic impulse response, reverberation time can be measured using Schroeder's backward integration method.
Felicia Lim +2 more
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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 0005 +4 more
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Super Resolution with Kernel Estimation and Dual Attention Mechanism
Information, 2020 Convolutional Neural Networks (CNN) have led to promising performance in super-resolution (SR). Most SR methods are trained and evaluated on predefined blur kernel datasets (e.g., bicubic).
Huan Liang +4 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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Motion Blur Kernel Estimation via Deep Learning
IEEE Transactions on Image Processing, 2018 The success of the state-of-the-art deblurring methods mainly depends on the restoration of sharp edges in a coarse-to-fine kernel estimation process. In this paper, we propose to learn a deep convolutional neural network for extracting sharp edges from blurred images.
Xiangyu Xu 0002 +3 more
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Applied Sciences, 2020 Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly.
Haoyuan Yang, Xiuqin Su, Songmao Chen
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Pixel-Level Kernel Estimation for Blind Super-Resolution
IEEE Access, 2021 Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high ...
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
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Cascaded Degradation-Aware Blind Super-Resolution
Sensors, 2023 Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation ...
Ding Zhang +3 more
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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.
FATTAL RAANAN, GOLDSTEIN AMIT
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