research-article

Noise brush: interactive high quality image-noise separation

Authors:

Chi-Keung Tang,

Jue WangAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 28, Issue 5

Pages 1 - 10

https://doi.org/10.1145/1618452.1618492

Published: 01 December 2009 Publication History

Abstract

This paper proposes an interactive approach using joint image-noise filtering for achieving high quality image-noise separation. The core of the system is our novel joint image-noise filter which operates in both image and noise domain, and can effectively separate noise from both high and low frequency image structures. A novel user interface is introduced, which allows the user to interact with both the image and the noise layer, and apply the filter adaptively and locally to achieve optimal results. A comprehensive and quantitative evaluation shows that our interactive system can significantly improve the initial image-noise separation results. Our system can also be deployed in various noise-consistent image editing tasks, where preserving the noise characteristics inherent in the input image is a desired feature.

Supplementary Material

Supplemental material. (146-chen.zip)

Download
31.13 MB

References

[1]

ABSoft Inc. 2008. Neat Image User Guide.

[2]

Adobe Systems. 2008. Adobe After Effects CS4 User Guide.

[3]

Adobe Systems. 2008. Adobe Photoshop CS4 User Guide.

[4]

Buades, A., Coll, B., and Morel, J.-M. 2008. Nonlocal image and movie denoising. International Journal of Computer Vision 76, 2, 123--139.

Digital Library

[5]

Dabov, K., Foi, A., Katkovnik, V., and Egiazarian, K. 2007. Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE TIP 16, 8, 2080--2095.

Digital Library

[6]

Efros, A., and Freeman, W. 2001. Image quilting for texture synthesis and transfer. In Proc. SIGGRAPH, 341--346.

Digital Library

[7]

Farbman, Z., Fattal, R., Lischinski, D., and Szeliski, R. 2008. Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27, 3, 67.

Digital Library

[8]

Imagenomic Inc. 2008. Noiseware User Guide.

[9]

Laroche, C., and Prescott, M. 1994. Apparatus and methods for adaptively interpolating a full color image utilizing chrominance gradients. U.S. patent 5,373,322.

[10]

Liu, C., Szeliski, R., Kang, S. B., Zitnick, C. L., and Freeman, W. T. 2008. Automatic estimation and removal of noise from a single image. IEEE TPAMI 30, 2, 299--314.

Digital Library

[11]

Mallat, S. 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE TPAMI 11, 7, 674--693.

Digital Library

[12]

Martin, D., Fowlkes, C., Tal, D., and Malik, J. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV.

[13]

Motwani, M., Gadiya, M., Motwani, R., and Frederick C. Harris, J. 2004. A survey of image denoising techniques. In Proc. of GSPx.

[14]

Perona, P., and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE TPAMI 12, 7, 629--639.

Digital Library

[15]

Petschnigg, G., Agrawala, M., Hoppe, H., Szeliski, R., Cohen, M., and Toyama, K. 2004. Digital photography with flash and no-flash image pairs. In ACM Trans. Graph., vol. 23, 664--672.

Digital Library

[16]

PictureCode Inc. 2008. Noise Ninjia User Guide.

[17]

Portilla, J., Strela, V., Wainwright, M., and Simoncelli, E. P. 2003. Image denoising using scale mixtures of gaussians in the wavelet domain. IEEE TIP 12(11), 1338--1351.

Digital Library

[18]

Roth, S., and Black, M. J. 2005. Fields of experts: A framework for learning image priors. In CVPR.

Digital Library

[19]

Salomon, D. 2005. Coding for Data and Computer Communications, 1 ed. Springer.

Digital Library

[20]

Simoncelli, E. P., and Adelson, E. H. 1996. Noise removal via Bayesian wavelet coring. In Proc 3rd IEEE Int'l Conf on Image Proc, IEEE Sig Proc Society, Lausanne, vol. I, 379--382.

[21]

Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In ICCV, 839--846.

Digital Library

[22]

Wang, Z., Bovik, A. C., Sheikh, H. R., Member, S., Simoncelli, E. P., and Member, S. 2004. Image quality assessment: From error visibility to structural similarity. IEEE TIP 13, 600--612.

Digital Library

[23]

Weiss, Y., and Freeman, B. 2007. What makes a good model of natural images. In CVPR.

Cited By

Zhai YLei JXia WHan SLiu FLi W(2020)Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution AlgorithmSensors10.3390/s2018518520:18(5185)Online publication date: 11-Sep-2020
https://doi.org/10.3390/s20185185
Jabbar MAhmad WWaqar AAbbas MPervaiz S(2020)Transformation based image de-noising2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)10.1109/iCoMET48670.2020.9074064(1-7)Online publication date: Jan-2020
https://doi.org/10.1109/iCoMET48670.2020.9074064
Yang JWang BWang MKe Y(2019)Unsupervised Co-segmentation of 3D Shapes Based on ComponentsProceedings of the 2nd International Conference on Computer Science and Software Engineering10.1145/3339363.3339386(89-95)Online publication date: 24-May-2019
https://dl.acm.org/doi/10.1145/3339363.3339386
Show More Cited By

Index Terms

Noise brush: interactive high quality image-noise separation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
      2. Computer vision tasks
  2. Computer graphics
    1. Image manipulation

Recommendations

Noise brush: interactive high quality image-noise separation
SIGGRAPH Asia '09: ACM SIGGRAPH Asia 2009 papers

This paper proposes an interactive approach using joint image-noise filtering for achieving high quality image-noise separation. The core of the system is our novel joint image-noise filter which operates in both image and noise domain, and can ...
Switching bilateral filter with a texture/noise detector for universal noise removal

In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median ...
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise

In this paper, we consider the restoration of images with signal-dependent noise. The filter is noise smoothing and adapts to local changes in image statistics based on a nonstationary mean, nonstationary variance (NMNV) image model. For images degraded ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 28, Issue 5

December 2009

646 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/1618452

Issue’s Table of Contents

Copyright © 2009 ACM.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2009

Published in TOG Volume 28, Issue 5

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Funding Sources

Research Grants Council, University Grants Committee, Hong Kong

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

77
Total Citations
View Citations
1,174
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhai YLei JXia WHan SLiu FLi W(2020)Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution AlgorithmSensors10.3390/s2018518520:18(5185)Online publication date: 11-Sep-2020
https://doi.org/10.3390/s20185185
Jabbar MAhmad WWaqar AAbbas MPervaiz S(2020)Transformation based image de-noising2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)10.1109/iCoMET48670.2020.9074064(1-7)Online publication date: Jan-2020
https://doi.org/10.1109/iCoMET48670.2020.9074064
Yang JWang BWang MKe Y(2019)Unsupervised Co-segmentation of 3D Shapes Based on ComponentsProceedings of the 2nd International Conference on Computer Science and Software Engineering10.1145/3339363.3339386(89-95)Online publication date: 24-May-2019
https://dl.acm.org/doi/10.1145/3339363.3339386
Carrière MMichel BOudot S(2018)Statistical analysis and parameter selection for mapperThe Journal of Machine Learning Research10.5555/3291125.329113719:1(478-516)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3291125.3291137
Harper JAgrawala M(2018)Converting Basic D3 Charts into Reusable Style TemplatesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2017.265974424:3(1274-1286)Online publication date: 1-Mar-2018
https://doi.org/10.1109/TVCG.2017.2659744
Ren ZDai PLiu SZhu SZeng B(2018)Coding Trajectory: Enable Video Coding for Video Denoising2018 25th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2018.8451492(3224-3228)Online publication date: Oct-2018
https://doi.org/10.1109/ICIP.2018.8451492
Zhou WTang KJia J(2018)S-LPMWorld Wide Web10.1007/s11280-018-0610-121:5(1425-1448)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11280-018-0610-1
Chen HLi M(2018)A novel mesh saliency approximation for polygonal mesh segmentationMultimedia Tools and Applications10.1007/s11042-017-5287-477:13(17223-17246)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11042-017-5287-4
Huska MLazzaro DMorigi S(2018)Shape Partitioning via $${L}_{p}$$Lp Compressed ModesJournal of Mathematical Imaging and Vision10.1007/s10851-018-0799-860:7(1111-1131)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10851-018-0799-8
Wang XZhou BWang ZZou DChen XZhao Q(2018)Efficiently consistent affinity propagation for 3D shapes co-segmentationThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-018-1538-234:6-8(997-1008)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s00371-018-1538-2
Show More Cited By

View Options

Get Access

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