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Extreme Channel Prior Embedded Network for Dynamic Scene Deblurring
[article]
2019
arXiv
pre-print
Preserved Fulltext
In this work, we propose an Extreme Channel Prior embedded Network (ECPeNet) to plug the extreme channel priors (i.e., priors on dark and bright channels) into a network architecture for effective dynamic ...
Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. ...
extreme (i.e., dark and bright) channel priors into a deep CNN for dynamic scene deblurring. ...
arXiv:1903.00763v1
fatcat:vtko6lhyi5cancefrjhxocflz4
A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2010.13313v1 fatcat:ydq26g2rwrf3xofl4nyjnjfn5q
This paper proposes a dark and bright channel prior guided deep network for retinal image quality assessment called GuidedNet. ...
Specifically, the dark and bright channel priors are embedded into the start layer of network to improve the discriminate ability of deep features. ...
deblurring [10] and dynamic scene deblurring [11] . ...
arXiv:2010.13313v2
fatcat:gyyvy772ofdwva3cqgxavw3q6e
Prior-enlightened and Motion-robust Video Deblurring
[article]
2020
arXiv
pre-print
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Therefore, we propose a PRiOr-enlightened and MOTION-robust video deblurring model (PROMOTION) suitable for challenging blurs. ...
On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information, explicitly enhancing the scenes' perception while mitigating the output's artifacts. ...
Similarly, [30] defines the bright channel prior and takes advantage of both bright and dark channel prior to deblur images. ...
arXiv:2003.11209v2
fatcat:7idg4c4b5beftpt5kblcsrg44a
Deep Motion Blur Removal Using Noisy/Blurry Image Pairs
[article]
2019
arXiv
pre-print
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Recent progress in deep neural networks suggests that kernel free single image deblurring can be efficiently performed, but questions about deblurring performance persist. ...
We evaluated the trained networks on a variety of synthetic datasets and real image pairs. ...
This network was motivated by the dark channel prior, where the dark channel map of a blurry image is less dark than that of the corresponding latent sharp image [25] , [26] . Pan et al. ...
arXiv:1911.08541v2
fatcat:untm7ndhn5ar3bui3hi45jyh5i
SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning
[article]
2021
arXiv
pre-print
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In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN. ...
For the first time in blind motion deblurring, we propose a sparse ResNet-block as a combination of sparse convolution layers and a trainable spatial pooler k-winner based on HTM (Hierarchical Temporal ...
[7] proposed a dynamic scene motion deblurring network that investigates the dark and bright channel image priors in the input blurry images. ...
arXiv:2111.04026v1
fatcat:phvtm3b53rhdppsnewmntf6vme
DAVANet: Stereo Deblurring with View Aggregation
[article]
2019
arXiv
pre-print
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In our proposed network, 3D scene cues from the depth and varying information from two views are incorporated, which help to remove complex spatially-varying blur in dynamic scenes. ...
However, they also suffer from blurry images in dynamic scenes which leads to visual discomfort and hampers further image processing. ...
Acknowledgements This work have been supported in part by the National Natural Science Foundation of China (No. 61671182 and 61872421) and Natural Science Foundation of Jiangsu Province (No. ...
arXiv:1904.05065v1
fatcat:nmq5kkfg2fdmjaqhsqycpald5m
Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network
[article]
2017
arXiv
pre-print
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All previous works rely on the deterministic imaging model where the color transformation stays the same regardless of the scene and thus they can only be applied for images taken under the manual mode ...
our method to improve the performance of image deblurring. ...
[29] , which is a blind image deblurring method that uses the dark channel prior. We use the source code from the authors website and the default settings except for the kernel size. ...
arXiv:1707.08350v1
fatcat:or3kqtofu5fknc27wyo7wdlx74
Deblurring Turbulent Images via Maximizing L1 Regularization
2021
Symmetry
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A blind image deblurring algorithm is needed, and a favorable image prior is the key to solving this problem. ...
Then, a novel soft suppression strategy is designed for the deblurring algorithm to inhibit artifacts. A coarse-to-fine scheme and a non-blind algorithm are also constructed. ...
Additionally, a dark and bright channel prior was also embedded into a network for dynamic scene deblurring [29] . ...
doi:10.3390/sym13081414
fatcat:peooua72yzegfhb3qkbqevggbm
Single Image Deblurring and Camera Motion Estimation with Depth Map
[article]
2019
arXiv
pre-print
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~In the real world, such blur is mainly caused by both the camera motion and the complex scene structure. ...
~We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem which is solved in an alternative manner. ...
Acknowledgements This work was supported in part by Natural Science Foundation of China grants (61871325, 61420106007, 61671387, 61603303) and the Australian Research Council (ARC) grants (DE140100180, ...
arXiv:1903.00231v1
fatcat:lsh664wqfrcr5g6lu5zhrsaegy
Spectral Norm Regularization for Blind Image Deblurring
2021
Symmetry
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Therefore, the SN of an image can effectively help image deblurring in various scenes, such as text, face, natural, and saturated images. ...
BDA-SN builds a deblurring estimator for the image degradation process by investigating the inherent properties of SN and an image gradient. ...
[33] developed an improved deep multi-patch hierarchical network that has a powerful and complex representation for dynamic scene deblurring. Almansour et al. ...
doi:10.3390/sym13101856
fatcat:cw2gd2upg5evbpvjvrvfatr3ia
Learn to model blurry motion via directional similarity and filtering
2018
Pattern Recognition
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In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. ...
Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches. ...
We thank Gabriel Brostow and the UCL PRISM Group for their helpful comments. ...
doi:10.1016/j.patcog.2017.04.020
fatcat:o4cm2qbmyngtnaukqauyddinx4
Learn to Model Motion from Blurry Footages
[article]
2017
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. ...
Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches. ...
We thank Gabriel Brostow and the UCL PRISM Group for their helpful comments. ...
arXiv:1704.05817v1
fatcat:idmih7ssr5aqflt26rhdnxm7xa
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
[article]
2018
arXiv
pre-print
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We also address the ill-posedness of deblurring by designing a network with a large receptive field and implemented via resampling to achieve a higher computational efficiency. ...
This lets a neural network choose during training what frame to output among the possible combinations. ...
MJ, GM, and PF acknowledge support from the Swiss National Science Foundation on projects 200021 153324 and 200021 165845. ...
arXiv:1804.04065v1
fatcat:cy7xmjzwdfattpza5abczcb2ta
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
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The file type is application/pdf.
We also address the ill-posedness of deblurring by designing a network with a large receptive field and implemented via resampling to achieve a higher computational efficiency. ...
This lets a neural network choose during training what frame to output among the possible combinations. ...
MJ, GM, and PF acknowledge support from the Swiss National Science Foundation on projects 200021 153324 and 200021 165845. ...
doi:10.1109/cvpr.2018.00663
dblp:conf/cvpr/JinMF18
fatcat:dxqqg6oogra5tatqlg3tu3lzja
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring
[article]
2019
arXiv
pre-print
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Therefore, the estimation of the blur kernel is essentially important for blind image deblurring. ...
Our approach is evaluated extensively on synthetic and real data and shows good results compared to the state-of-the-art deblurring approaches. ...
Acknowledgement This research was supported in part by Australia Centre for Robotic Vision (CE140100016), the Australian Research Council grants (DE140100180, DE180100628) and the Natural Science Foundation ...
arXiv:1811.10185v3
fatcat:xted4brixfejhpgufzpciwt4na
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