Parametric model for image blur kernel estimation
2018 International Conference on Orange Technologies (ICOT), 2018This paper we propose an novel parametric approach for single image kernel estimation with both motion blur and Gaussian blur coupled. In the view of that daily pictures captured by handheld device usually contain motion blur and defocus simultaneously.
Jinqiu Sun, Yu Zhu, Yanning Zhang
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A Coarse-to-fine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks
2019exaly
Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization [PDF]
Sensors, 2021 Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors.
Hongshan Zhao +2 more
doaj +5 more sources
Blur Kernel Estimation by Structure Sparse Prior [PDF]
Applied Sciences, 2020 Blind image deblurring tries to recover a sharp version from a blurred image, where blur kernel is usually unknown. Recently, sparse representation has been successfully applied to estimate the blur kernel.
Xiaobin Yuan, Jingping Zhu, Xiaobin Li
doaj +5 more sources
Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
Applied Sciences, 2022 Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images.
Shanshan Liu +2 more
doaj +4 more sources
Multiregression spatially variant blur kernel estimation based on inter‐kernel consistency
Electronics Letters, 2023 Most single‐image super‐resolution (SR) models suffer from the degradation of image restoration performance when restoring a high‐resolution (HR) image from a low‐resolution (LR) image downscaled using an unknown blur kernel. The spatially invariant blur
Min Hyuk Kim +2 more
doaj +3 more sources
Robust Motion Blur Kernel Estimation by Kernel Continuity Prior [PDF]
IEEE Access, 2020 The accurate kernel estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for kernel estimation.
Xueling Chen +3 more
doaj +4 more sources
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
openaire +4 more sources
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
openaire +3 more sources
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
doaj +2 more sources