research-article

A no-reference metric for evaluating the quality of motion deblurring

Authors:

Adam Finkelstein,

Szymon RusinkiewiczAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 32, Issue 6

Article No.: 175, Pages 1 - 12

https://doi.org/10.1145/2508363.2508391

Published: 01 November 2013 Publication History

Abstract

Methods to undo the effects of motion blur are the subject of intense research, but evaluating and tuning these algorithms has traditionally required either user input or the availability of ground-truth images. We instead develop a metric for automatically predicting the perceptual quality of images produced by state-of-the-art deblurring algorithms. The metric is learned based on a massive user study, incorporates features that capture common deblurring artifacts, and does not require access to the original images (i.e., is "noreference"). We show that it better matches user-supplied rankings than previous approaches to measuring quality, and that in most cases it outperforms conventional full-reference image-similarity measures. We demonstrate applications of this metric to automatic selection of optimal algorithms and parameters, and to generation of fused images that combine multiple deblurring results.

References

[1]

Bradley, R. A., and Terry, M. E. 1952. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika 39, 3/4.

[2]

Cadik, M., Herzog, R., Mantiuk, R., Myszkowski, K., and Seidel, H.-P. 2012. New measurements reveal weaknesses of image quality metrics in evaluating graphics artifacts. ACM Trans. Graphics 31, 6 (Nov.).

Digital Library

[3]

Chen, X., Golovinskiy, A., and Funkhouser, T. 2009. A benchmark for 3D mesh segmentation. ACM Trans. Graphics 28, 3.

Digital Library

[4]

Cho, S., and Lee, S. 2009. Fast motion deblurring. ACM Trans. Graphics 28, 5.

Digital Library

[5]

Cho, T. S., Paris, S., Horn, B., and Freeman, W. 2011. Blur kernel estimation using the Radon transform. In Proc. CVPR 2011.

Digital Library

[6]

Cole, F., Sanik, K., DeCarlo, D., Finkelstein, A., Funkhouser, T., Rusinkiewicz, S., and Singh, M. 2009. How well do line drawings depict shape? ACM Trans. Graphics 28, 3.

Digital Library

[7]

Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Processing 16, 8.

Digital Library

[8]

Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. 2006. Removing camera shake from a single photograph. ACM Trans. Graphics 25, 3.

Digital Library

[9]

Field, D. J., and Brady, N. 1997. Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vision Research 37, 23.

[10]

Goldstein, A., and Fattal, R. 2012. Blur-kernel estimation from spectral irregularities. In Proc. ECCV 2012.

Digital Library

[11]

Gupta, A., Joshi, N., Zitnick, L., Cohen, M., and Curless, B. 2010. Single image deblurring using motion density functions. In Proc. ECCV 2010.

Digital Library

[12]

Hassen, R., Wang, Z., and Salama, M. 2010. No-reference image sharpness assessment based on local phase coherence measurement. In Proc. ICASSP 2010.

[13]

Hilbe, J. M. 2009. Logistic Regression Models. Chapman & Hall/CRC Press.

[14]

Ji, H., and Wang, K. 2012. A two-stage approach to blind spatially-varying motion deblurring. In Proc. CVPR 2012.

Digital Library

[15]

Joshi, N., Szeliski, R., and Kriegman, D. 2008. PSF estimation using sharp edge prediction. In Proc. CVPR 2008.

[16]

Joshi, N., Zitnick, C., Szeliski, R., and Kriegman, D. 2009. Image deblurring and denoising using color priors. In Proc. CVPR 2009.

[17]

Kendall, M. G. 1938. A new measure of rank correlation. Biometrika 30, 1/2.

[18]

Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., and Harmeling, S. 2012. Recording and playback of camera shake: Benchmarking blind deconvolution with a real-world database. In Proc. ECCV 2012.

Digital Library

[19]

Krishnan, D., and Fergus, R. 2009. Fast image deconvolution using hyper-Laplacian priors. In Proc. NIPS 2009.

[20]

Krishnan, D., Tay, T., and Fergus, R. 2011. Blind deconvolution using a normalized sparsity measure. In Proc. CVPR 2011.

Digital Library

[21]

Levin, A., Fergus, R., Durand, F., and Freeman, W. T. 2007. Image and depth from a conventional camera with a coded aperture. ACM Trans. Graphics 26, 3.

Digital Library

[22]

Levin, A., Weiss, Y., Durand, F., and Freeman, W. 2011. Efficient marginal likelihood optimization in blind deconvolution. In Proc. CVPR 2011.

Digital Library

[23]

Mantiuk, R., Kim, K. J., Rempel, A. G., and Heidrich, W. 2011. HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. ACM Trans. Graphics 30, 4.

Digital Library

[24]

Marziliano, P., Dufaux, F., Winkler, S., and Ebrahimi, T. 2004. Perceptual blur and ringing metrics: Application to JPEG2000. Signal Processing: Image Communication 19, 2.

[25]

Masia, B., Presa, L., Corrales, A., and Gutierrez, D. 2012. Perceptually optimized coded apertures for defocus deblurring. Computer Graphics Forum 31, 6.

Digital Library

[26]

Mittal, A., Moorthy, A. K., and Bovik, A. C. 2012. Automatic parameter prediction for image denoising algorithms using perceptual quality features. In Proc. SPIE, vol. 8291.

[27]

Mittal, A., Moorthy, A., and Bovik, A. 2012. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Processing 21, 12.

Digital Library

[28]

Mittal, A., Soundararajan, R., and Bovik, A. 2012. Making a "completely blind" image quality analyzer. IEEE Signal Processing Letters PP, 99.

[29]

Moorthy, A., and Bovik, A. 2010. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters 17, 5.

[30]

Narvekar, N., and Karam, L. 2011. A no-reference image blur metric based on the cumulative probability of blur detection (CPBD). IEEE Trans. Image Processing 20, 9.

Digital Library

[31]

Pérez, P., Gangnet, M., and Blake, A. Poisson image editing. ACM Trans. Graphics 22, 3.

Digital Library

[32]

Saad, M., Bovik, A., and Charrier, C. 2010. A DCT statistics-based blind image quality index. IEEE Signal Processing Letters 17, 6.

[33]

Samadani, R., Mauer, T. A., Berfanger, D. M., and Clark, J. H. 2010. Image thumbnails that represent blur and noise. IEEE Trans. Image Processing 19, 2 (Feb.).

Digital Library

[34]

Schuler, C. J., Hirsch, M., Harmeling, S., and Schölkopf, B. 2012. Blind correction of optical aberrations. In Proceedings of the 12th European conference on Computer Vision - Volume Part III, Springer-Verlag, Berlin, Heidelberg, Proc. ECCV 2012, 187--200.

Digital Library

[35]

Secord, A., Lu, J., Finkelstein, A., Singh, M., and Nealen, A. 2011. Perceptual models of viewpoint preference. ACM Trans. Graphics 30, 5.

Digital Library

[36]

Shan, Q., Jia, J., and Agarwala, A. 2008. High-quality motion deblurring from a single image. ACM Trans. Graphics 27, 3.

Digital Library

[37]

Sheikh, H., and Bovik, A. 2006. Image information and visual quality. IEEE Trans. Image Processing 15, 2. Spearman, C. 1904. The proof and measurement of association between two things. The American journal of psychology 15, 1.

Digital Library

[38]

Tang, H., Joshi, N., and Kapoor, A. 2011. Learning a blind measure of perceptual image quality. In Proc. CVPR 2011.

Digital Library

[39]

Teo, P., and Heeger, D. 1994. Perceptual image distortion. In Proc. ICIP 1994, vol. 2.

[40]

Trentacoste, M., Mantiuk, R., and Heidrich, W. 2011. Blur-Aware Image Downsizing. In Proc. Eurographics.

[41]

Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Processing 13, 4.

Digital Library

[42]

Whyte, O., Sivic, J., Zisserman, A., and Ponce, J. 2010. Non-uniform deblurring for shaken images. In Proc. CVPR 2010.

[43]

Xu, L., and Jia, J. 2010. Two-phase kernel estimation for robust motion deblurring. In Proc. ECCV 2010.

Digital Library

[44]

Ye, P., Kumar, J., Kang, L., and Doermann, D. 2012. Unsupervised feature learning framework for no-reference image quality assessment. In Proc. CVPR 2012.

Digital Library

[45]

Yuan, L., Sun, J., Quan, L., and Shum, H.-Y. 2007. Image deblurring with blurred/noisy image pairs. ACM Trans. Graphics 26, 3.

Digital Library

[46]

Zhu, X., and Milanfar, P. 2009. A no-reference sharpness metric sensitive to blur and noise. In Quality of Multimedia Experience 2009.

[47]

Zhu, X., and Milanfar, P. 2010. Automatic parameter selection for denoising algorithms using a no-reference measure of image content. IEEE Trans. Image Processing 19, 12.

Digital Library

[48]

Zoran, D., and Weiss, Y. 2011. From learning models of natural image patches to whole image restoration. In Proc. ICCV 2011.

Digital Library

Cited By

Ding KZhong RWang ZYu YFang Y(2024)Adaptive Structure and Texture Similarity Metric for Image Quality Assessment and OptimizationIEEE Transactions on Multimedia10.1109/TMM.2023.333320826(5398-5409)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3333208
Rong JHuang HLi J(2024)IMU-Assisted Accurate Blur Kernel Re-Estimation in Non-Uniform Camera Shake DeblurringIEEE Transactions on Image Processing10.1109/TIP.2024.341181933(3823-3838)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3411819
Genze NWirth MSchreiner CAjekwe RGrieb MGrimm D(2023)Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation modelPlant Methods10.1186/s13007-023-01060-819:1Online publication date: 22-Aug-2023
https://doi.org/10.1186/s13007-023-01060-8
Show More Cited By

Index Terms

A no-reference metric for evaluating the quality of motion deblurring
1. Computing methodologies
  1. Computer graphics

Recommendations

Fast motion deblurring
SIGGRAPH Asia '09: ACM SIGGRAPH Asia 2009 papers

This paper presents a fast deblurring method that produces a deblurring result from a single image of moderate size in a few seconds. We accelerate both latent image estimation and kernel estimation in an iterative deblurring process by introducing a ...
Fast motion deblurring

This paper presents a fast deblurring method that produces a deblurring result from a single image of moderate size in a few seconds. We accelerate both latent image estimation and kernel estimation in an iterative deblurring process by introducing a ...
A No-Reference Quality Metric for Blur Image
GREENCOM-ITHINGS-CPSCOM '13: Proceedings of the 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing

In this paper, we address the problem of no-reference quality assessment for blurred picture from digital camera. To evaluate the digital picture corrupted by defocus and motion artifact, the inherent information of blur kernel is estimated from the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 32, Issue 6

November 2013

671 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2508363

Issue’s Table of Contents

Copyright © 2013 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 the author(s) 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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2013

Published in TOG Volume 32, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

81
Total Citations
View Citations
751
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)5

Reflects downloads up to 21 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ding KZhong RWang ZYu YFang Y(2024)Adaptive Structure and Texture Similarity Metric for Image Quality Assessment and OptimizationIEEE Transactions on Multimedia10.1109/TMM.2023.333320826(5398-5409)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3333208
Rong JHuang HLi J(2024)IMU-Assisted Accurate Blur Kernel Re-Estimation in Non-Uniform Camera Shake DeblurringIEEE Transactions on Image Processing10.1109/TIP.2024.341181933(3823-3838)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3411819
Genze NWirth MSchreiner CAjekwe RGrieb MGrimm D(2023)Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation modelPlant Methods10.1186/s13007-023-01060-819:1Online publication date: 22-Aug-2023
https://doi.org/10.1186/s13007-023-01060-8
Sheng YZhang LLi Y(2023)Simulating human visual perception behaviors for video quality assessmentJournal of Electronic Imaging10.1117/1.JEI.32.6.06250832:06Online publication date: 1-Nov-2023
https://doi.org/10.1117/1.JEI.32.6.062508
Shao W(2023)Revisiting the Regularizers in Blind Image Deblurring With a New OneIEEE Transactions on Image Processing10.1109/TIP.2023.328035832(3994-4009)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3280358
Yu XXie W(2023)Blurry Facial-Image Deconvolution via Model-Guided Deep Neural Network Inspired From Edge RegularizationIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.315776615:1(285-297)Online publication date: Mar-2023
https://doi.org/10.1109/TCDS.2022.3157766
Fu MZhao YHou Z(2023)An effective interpretation of defocusing and the corresponding defocus convolution kernelOptics & Laser Technology10.1016/j.optlastec.2022.109035160(109035)Online publication date: May-2023
https://doi.org/10.1016/j.optlastec.2022.109035
Rahimi-Ajdadi FMollazade K(2023)Image deblurring to improve the grain monitoring in a rice combine harvesterSmart Agricultural Technology10.1016/j.atech.2023.1002194(100219)Online publication date: Aug-2023
https://doi.org/10.1016/j.atech.2023.100219
Dimlo UNarasimharao JLaxmaiah BSrinath ERani DSandhyarani Kumar V(2023)An Improved Blind Deconvolution for Restoration of Blurred Images Using Ringing Removal ProcessingProceedings of Fourth International Conference on Computer and Communication Technologies10.1007/978-981-19-8563-8_34(357-366)Online publication date: 30-Mar-2023
https://doi.org/10.1007/978-981-19-8563-8_34
Slutsky M(2022)Noise-Adaptive Non-Blind Image DeblurringSensors10.3390/s2218692322:18(6923)Online publication date: 13-Sep-2022
https://doi.org/10.3390/s22186923
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents