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DC-Deblur: A Dilated Convolutional Network for Single Image Deblurring
2021Single image deblurring is a significant and challenging task in image processing vision and machine learning. Convolutional Neural Network (CNN) based models for deblurring often have a complex structure and a considerable number of parameters compared with those for other image restoration tasks such as image denoising, dehazing and super-resolution.
Boyan Xu, Hujun Yin
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Journal of Electronic Imaging, 2014
We propose an algorithm to recover the latent image from the blurred and compressed input. In recent years, although many image deblurring algorithms have been proposed, most of the previous methods do not consider the compression effect in blurry images. Actually, it is unavoidable in practice that most of the real-world images are compressed.
Yuquan Xu, Xiyuan Hu, Silong Peng
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We propose an algorithm to recover the latent image from the blurred and compressed input. In recent years, although many image deblurring algorithms have been proposed, most of the previous methods do not consider the compression effect in blurry images. Actually, it is unavoidable in practice that most of the real-world images are compressed.
Yuquan Xu, Xiyuan Hu, Silong Peng
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2012
The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent works focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structures on estimating blur kernels.
Zhe Hu, Ming-Hsuan Yang 0001
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The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image. Recent works focus on exploiting natural image priors or additional image observations for deblurring, but pay less attention to the influence of image structures on estimating blur kernels.
Zhe Hu, Ming-Hsuan Yang 0001
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2013 IEEE International Conference on Computer Vision, 2013
Most conventional single image deblurring methods assume that the underlying scene is static and the blur is caused by only camera shake. In this paper, in contrast to this restrictive assumption, we address the deblurring problem of general dynamic scenes which contain multiple moving objects as well as camera shake.
Tae Hyun Kim 0006 +2 more
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Most conventional single image deblurring methods assume that the underlying scene is static and the blur is caused by only camera shake. In this paper, in contrast to this restrictive assumption, we address the deblurring problem of general dynamic scenes which contain multiple moving objects as well as camera shake.
Tae Hyun Kim 0006 +2 more
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2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
A histogram is an effective form for extracting various types of features and has been attracting keen attention in pattern recognition fields. In visual recognition, however, the histogram features suffer from smoothing due to the processes of quantizing continuous input patterns into discrete codes and pooling them.
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A histogram is an effective form for extracting various types of features and has been attracting keen attention in pattern recognition fields. In visual recognition, however, the histogram features suffer from smoothing due to the processes of quantizing continuous input patterns into discrete codes and pooling them.
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2014
A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research.
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A comprehensive guide to restoring images degraded by motion blur, bridging the traditional approaches and emerging computational photography-based techniques, and bringing together a wide range of methods emerging from basic theory as well as cutting-edge research.
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Binary Tomography with Deblurring
2006We study two scenarios of limited-angle binary tomography with data distorted with an unknown convolution: Either the projection data are taken from a blurred object, or the projection data themselves are blurred. These scenarios are relevant in case of scattering and due to a finite resolution of the detectors.
Stefan Weber +3 more
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Rt-Deblur: Real-Time Image Deblurring for Object Detection
SSRN Electronic Journal, 2022Hanzhao Wang +3 more
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2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2017
Fu-Wen Yang, Hwei-Jen Lin, Hua Chuang
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Fu-Wen Yang, Hwei-Jen Lin, Hua Chuang
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Residual Diffusion Deblurring Model for Single Image Defocus Deblurring
Proceedings of the AAAI Conference on Artificial IntelligenceDefocus deblurring is a challenging task due to the spatially varying nature of defocus blur with multiple plausible solutions of a single given image. However, most existing methods falter when faced with extensive and variable defocus blur, either ignoring it or relying on additional loss functions to enhance perceptual quality. This often results in
Haoxuan Feng +4 more
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