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Removing camera shake from a single photograph

Authors:

Aaron Hertzmann,

William T. FreemanAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 25, Issue 3

Pages 787 - 794

https://doi.org/10.1145/1141911.1141956

Published: 01 July 2006 Publication History

Abstract

Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency-domain constraints on images, or overly simplified parametric forms for the motion path during camera shake. Real camera motions can follow convoluted paths, and a spatial domain prior can better maintain visually salient image characteristics. We introduce a method to remove the effects of camera shake from seriously blurred images. The method assumes a uniform camera blur over the image and negligible in-plane camera rotation. In order to estimate the blur from the camera shake, the user must specify an image region without saturation effects. We show results for a variety of digital photographs taken from personal photo collections.

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Cited By

Arslan AGultekin GSaranli A(2024)IMU-aided adaptive mesh-grid based video motion deblurringPeerJ Computer Science10.7717/peerj-cs.254010(e2540)Online publication date: 25-Nov-2024
https://doi.org/10.7717/peerj-cs.2540
Saqib MDawood HAlghamdi ADawood H(2024)Weber's Law-based Regularization for Blind Image DeblurringEngineering, Technology & Applied Science Research10.48084/etasr.657614:1(12937-12943)Online publication date: 8-Feb-2024
https://doi.org/10.48084/etasr.6576
Wang ZLiu HCheng LLyu ZGao LJiang NHe ZLiu Y(2024)Improvement of Whole-body Bone Planar Images on a Bone-dedicated Single-photon Emission Computed Tomography Scanner by Blind Deconvolution AlgorithmJournal of Medical Physics10.4103/jmp.jmp_127_2349:1(110-119)Online publication date: 30-Mar-2024
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Index Terms

Removing camera shake from a single photograph
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
2. Mathematics of computing
  1. Probability and statistics

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Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 25, Issue 3

July 2006

742 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1141911

Issue’s Table of Contents

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 July 2006

Published in TOG Volume 25, Issue 3

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Cited By

Arslan AGultekin GSaranli A(2024)IMU-aided adaptive mesh-grid based video motion deblurringPeerJ Computer Science10.7717/peerj-cs.254010(e2540)Online publication date: 25-Nov-2024
https://doi.org/10.7717/peerj-cs.2540
Saqib MDawood HAlghamdi ADawood H(2024)Weber's Law-based Regularization for Blind Image DeblurringEngineering, Technology & Applied Science Research10.48084/etasr.657614:1(12937-12943)Online publication date: 8-Feb-2024
https://doi.org/10.48084/etasr.6576
Wang ZLiu HCheng LLyu ZGao LJiang NHe ZLiu Y(2024)Improvement of Whole-body Bone Planar Images on a Bone-dedicated Single-photon Emission Computed Tomography Scanner by Blind Deconvolution AlgorithmJournal of Medical Physics10.4103/jmp.jmp_127_2349:1(110-119)Online publication date: 30-Mar-2024
https://doi.org/10.4103/jmp.jmp_127_23
Wu TFeng CLi Z(2024)Blind image deblurring using kernel error for p-shrinkage operator optimization modelInverse Problems and Imaging10.3934/ipi.2024003(0-0)Online publication date: 2024
https://doi.org/10.3934/ipi.2024003
Ho QDuong MLee SHong M(2024)EHNet: Efficient Hybrid Network with Dual Attention for Image DeblurringSensors10.3390/s2420654524:20(6545)Online publication date: 10-Oct-2024
https://doi.org/10.3390/s24206545
Zhu MLiu JWang F(2024)Blind Deblurring Method for CASEarth Multispectral Images Based on Inter-Band Gradient Similarity PriorSensors10.3390/s2419625924:19(6259)Online publication date: 27-Sep-2024
https://doi.org/10.3390/s24196259
Salah FMoussaid NAbassi AJadir A(2024)Towards a Nash game strategy approach to blind image deconvolution: a fractional-order derivative variational frameworkMathematical Modeling and Computing10.23939/mmc2024.03.68211:3(682-691)Online publication date: 2024
https://doi.org/10.23939/mmc2024.03.682
Dong WWang QTao STian C(2024)Blind multi-Poissonian image deconvolution with sparse log-step gradient priorOptics Express10.1364/OE.51360432:6(9061)Online publication date: 28-Feb-2024
https://doi.org/10.1364/OE.513604
Zhangying HQuan ZMingju CJingcheng CXiaofu WBaoyu Z(2024)Survey of image deblurringJournal of Image and Graphics10.11834/jig.23055529:4(841-861)Online publication date: 2024
https://doi.org/10.11834/jig.230555
Shi XLin SYang MHuang CLee J(2024)Image deblurring by multi-scale modified U-Net using dilated convolutionScience Progress10.1177/00368504241231161107:1Online publication date: 23-Feb-2024
https://doi.org/10.1177/00368504241231161
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