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Region-Adaptive Dense Network for Efficient Motion Deblurring
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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This capability is complemented by a self-attentive module which captures non-local spatial relationships among the intermediate features and enhances the spatially varying processing capability. ...
Our network facilitates interpretable modeling of the spatially-varying deblurring process while dispensing with multi-scale processing and large filters entirely. ...
Conclusions We proposed an efficient motion deblurring architecture composed of convolutional modules that enable spatially adaptive feature learning through filter transformations and feature attention ...
doi:10.1609/aaai.v34i07.6862
fatcat:pu6mz3jasfexnidcg3rawsrf5q
Motion Deblurring with an Adaptive Network
[article]
2022
arXiv
pre-print
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Our networks can implicitly model the spatially-varying deblurring process, while dispensing with multi-scale processing and large filters entirely. ...
This capability is complemented by a self-attentive module which captures non-local relationships among the intermediate features and enhances the receptive field. ...
Conclusions We proposed efficient image and video deblurring architectures composed of convolutional modules that enable spatially adaptive feature learning through filter transformations and feature attention ...
arXiv:1903.11394v4
fatcat:jgsssxep6vfbnafshrg2x6ijke
Adaptive Single Image Deblurring
[article]
2022
arXiv
pre-print
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In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations within and across different images. ...
We use a patch hierarchical attentive architecture composed of the above module that implicitly discover the spatial variations in the blur present in the input image and in turn perform local and global ...
We propose an efficient deblurring design built on new convolutional modules that learn transformation of
features using global attention and dynamic local filters. ...
arXiv:2201.00155v1
fatcat:jexcg67nqfgefi63zja27aj2xe
Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring
[article]
2020
arXiv
pre-print
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In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. ...
This paper tackles the problem of motion deblurring of dynamic scenes. ...
The major contributions of this work are: • We propose an efficient deblurring design built on new convolutional modules that learn the transformation of features using global attention and adaptive local ...
arXiv:2004.05343v1
fatcat:jhsglmlubzb5ja4s7omfxasfo4
Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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In this work, we propose an efficient pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively. ...
This paper tackles the problem of motion deblurring of dynamic scenes. ...
The major contributions of this work are: • We propose an efficient deblurring design built on new convolutional modules that learn the transformation of features using global attention and adaptive local ...
doi:10.1109/cvpr42600.2020.00366
dblp:conf/cvpr/SuinPR20
fatcat:rv6ptq2x35g5phvepetqe3qkqi
Flow-Guided Sparse Transformer for Video Deblurring
[article]
2022
arXiv
pre-print
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In this paper, we propose a novel framework, Flow-Guided Sparse Transformer (FGST), for video deblurring. ...
Exploiting similar and sharper scene patches in spatio-temporal neighborhoods is critical for video deblurring. ...
Acknowledgements: This work is partially supported by the NSFC fund (61831014), the Shenzhen Science and Technology Project under Grant (CJGJZD20200617102601004, JSGG20210802153150005). ...
arXiv:2201.01893v3
fatcat:pg5w2bndbngydl2ynjek4x4rja
Single Image Joint Motion Deblurring and Super-Resolution Using the Multi-Scale Channel Attention Modules
2021
Mathematical Problems of Computer Science
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The implementation code and the pre-trained model are publicly available at https://github.com/misakshoyan/joint-motion-deblur-and-sr. ...
The deblurring subnetwork is based on multi-stage progressive architecture, while the super-resolution subnetwork is designed using the multi-scale channel attention modules. ...
To solve the motion deblurring and SR problems jointly, the network should extract both contextually and spatially informative features. ...
doi:10.51408/1963-0076
fatcat:ulnois4kvzggvclqywynfcfcfy
Stripformer: Strip Transformer for Fast Image Deblurring
[article]
2022
arXiv
pre-print
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reweight image features in the horizontal and vertical directions to catch blurred patterns with different orientations. ...
In addition to detecting region-specific blurred patterns of various orientations and magnitudes, Stripformer is also a token-efficient and parameter-efficient transformer model, demanding much less memory ...
Hence, transformers with multi-head self-attention to explore local and global correlations would be a good choice for deblurring. ...
arXiv:2204.04627v2
fatcat:xqdoryx4ufcjreoaqwhu2bwt2e
NTIRE 2020 Challenge on Image and Video Deblurring
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. ...
The winning methods demonstrate the state-ofthe-art performance on image and video deblurring tasks. ...
., DisneyResearch|Studios, and ETH Zurich (Computer Vision Lab). ...
doi:10.1109/cvprw50498.2020.00216
dblp:conf/cvpr/NahSTLTXCTBSXCS20
fatcat:a6ojyfuidrbb3avwdpv4mje77e
Restormer: Efficient Transformer for High-Resolution Image Restoration
[article]
2022
arXiv
pre-print
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While the Transformer model mitigates the shortcomings of CNNs (i.e., limited receptive field and inadaptability to input content), its computational complexity grows quadratically with the spatial resolution ...
In this work, we propose an efficient Transformer model by making several key designs in the building blocks (multi-head attention and feed-forward network) such that it can capture long-range pixel interactions ...
Special thanks to Abdullah Abuolaim and Zhendong Wang for providing the results. ...
arXiv:2111.09881v2
fatcat:zzmue7de3feergl5ciry25r3qm
NTIRE 2019 Challenge on Video Deblurring: Methods and Results
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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This paper reviews the first NTIRE challenge on video deblurring (restoration of rich details and high frequency components from blurred video frames) with focus on the proposed solutions and results. ...
Track 1 employed dynamic motion blurs while Track 2 had additional MPEG video compression artifacts. Each competition had 109 and 93 registered participants. ...
., and ETH Zurich. ...
doi:10.1109/cvprw.2019.00249
dblp:conf/cvpr/NahTBHMSL19
fatcat:eieq2a6ms5bn7hx6cpccozhuyi
NTIRE 2020 Challenge on Image and Video Deblurring
[article]
2020
arXiv
pre-print
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Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. ...
The winning methods demonstrate the state-ofthe-art performance on image and video deblurring tasks. ...
., DisneyResearch|Studios, and ETH Zurich (Computer Vision Lab). ...
arXiv:2005.01244v2
fatcat:aoy3tyxlybefrd7yd5ywvr6jh4
Efficient Video Deblurring Guided by Motion Magnitude
[article]
2022
arXiv
pre-print
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The MMP consists of both spatial and temporal blur level information, which can be further integrated into an efficient recurrent neural network (RNN) for video deblurring. ...
Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur. ...
Acknowledgements This paper is supported by JSPS KAKENHI Grant Numbers 22H00529 and 20H05951. ...
arXiv:2207.13374v1
fatcat:7r2me7362bad5a5agmddddwfbq
Attention Network for Non-Uniform Deblurring
2020
IEEE Access
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In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. In this paper, we investigate an attention network for image deblurring. ...
Therefore, we propose a novel dense feature fusion block that consists of a channel attention module and a pixel attention module. ...
Specifically, each attention block contains spatial attention module and channel attention module. ...
doi:10.1109/access.2020.2997408
fatcat:oyis2k6zx5bvxembvkibpc7f5q
Blind Non-Uniform Motion Deblurring using Atrous Spatial Pyramid Deformable Convolution and Deblurring-Reblurring Consistency
[article]
2022
arXiv
pre-print
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Hence, non-uniform motion deblurring is still a challenging and open problem. ...
Many deep learning based methods are designed to remove non-uniform (spatially variant) motion blur caused by object motion and camera shake without knowing the blur kernel. ...
In addition to the above model, some works model the non-uniform motion deblurring as a linear transformation. Indeed, Bahat et al. ...
arXiv:2106.14336v2
fatcat:67okpikuzzfzxpu7nvgzu2gpvu
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