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Optics Letters, 2023
Recently, imaging systems have exhibited remarkable image restoration performance through optimized optical systems and deep-learning-based models. Despite advancements in optical systems and models, severe performance degradation occurs when the predefined optical blur kernel differs from the actual kernel while restoring and upscaling the images ...
Jun-Seok, Yun, Seok, Bong Yoo
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Recently, imaging systems have exhibited remarkable image restoration performance through optimized optical systems and deep-learning-based models. Despite advancements in optical systems and models, severe performance degradation occurs when the predefined optical blur kernel differs from the actual kernel while restoring and upscaling the images ...
Jun-Seok, Yun, Seok, Bong Yoo
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Robust Image Deblurring With an Inaccurate Blur Kernel
IEEE Transactions on Image Processing, 2012Most existing nonblind image deblurring methods assume that the blur kernel is free of error. However, it is often unavoidable in practice that the input blur kernel is erroneous to some extent. Sometimes, the error could be severe, e.g., for images degraded by nonuniform motion blurring. When an inaccurate blur kernel is used as the input, significant
Ji, H., Wang, K.
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IMU-Assisted Accurate Blur Kernel Re-Estimation in Non-Uniform Camera Shake Deblurring
IEEE Transactions on Image ProcessingImage deblurring for camera shake is a highly regarded problem in the field of computer vision. A promising solution is the patch-wise non-uniform image deblurring algorithms, where a linear transformation model is typically established between different
Jian-Xiang Rong, Hua Huang, Jia Li
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Space-variant blur kernel estimation and image deblurring through kernel clustering
Signal Processing: Image Communication, 2019Abstract This paper presents a space-variant blur kernel estimation and image deblurring framework. For space-variant blur kernel estimation, the input image is divided into small patches, and for each patch, the blur kernel is estimated. The estimated kernels are then grouped to determine different kernel clusters in the image.
Alam, Muhammad Zeshan +2 more
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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.
Ao Zhang +4 more
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Automatic blur-kernel-size estimation for motion deblurring
The Visual Computer, 2014Existing image deblurring approaches often take the blur-kernel-size as an important manual parameter. When set improperly, this parameter can lead to significant errors in the estimated blur kernels. However, manually specifying a proper kernel size for an input image is usually a tedious trial-and-error process.
Shaoguo Liu +4 more
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Inertial sensor aided motion blur kernel estimation for cooled IR detector
Optics and Lasers in EngineeringKaustubh Saurabh Singh +3 more
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Estimation of motion blur kernel parameters using regression convolutional neural networks
J. Electronic Imaging, 2023. Many deblurring and blur kernel estimation methods use a maximum a posteriori approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel.
Luis G. Varela +4 more
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Event-based Blur Kernel Estimation For Blind Motion Deblurring
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023Motion blur can significantly reduce the quality of images, and researchers have developed various algorithms to address this issue. One common approach to deblurring is to use deconvolution to cancel out the blur effect, but this method is limited by ...
Takuya Nakabayashi +3 more
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Blind Super-Resolution on Remote Sensing Images with Blur Kernel Prediction
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021Single image super-resolution (SISR) is essential in many remote sensing applications. Most of the existing SISR methods on remote sensing images assume that the low resolution (LR) images are synthesized from high-resolution (HR) images by bicubic ...
Runmin Dong, Lixian Zhang, H. Fu
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