Jia Chen
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.
Supplemental Material
Available for Download
- ABSoft Inc. 2008. Neat Image User Guide.Google Scholar
- Adobe Systems. 2008. Adobe After Effects CS4 User Guide.Google Scholar
- Adobe Systems. 2008. Adobe Photoshop CS4 User Guide.Google Scholar
- Buades, A., Coll, B., and Morel, J.-M. 2008. Nonlocal image and movie denoising. International Journal of Computer Vision 76, 2, 123--139. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Efros, A., and Freeman, W. 2001. Image quilting for texture synthesis and transfer. In Proc. SIGGRAPH, 341--346. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Imagenomic Inc. 2008. Noiseware User Guide.Google Scholar
- 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.Google Scholar
- 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. Google ScholarDigital Library
- Mallat, S. 1989. A theory for multiresolution signal decomposition: The wavelet representation. IEEE TPAMI 11, 7, 674--693. Google ScholarDigital Library
- 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.Google Scholar
- Motwani, M., Gadiya, M., Motwani, R., and Frederick C. Harris, J. 2004. A survey of image denoising techniques. In Proc. of GSPx.Google Scholar
- Perona, P., and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE TPAMI 12, 7, 629--639. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- PictureCode Inc. 2008. Noise Ninjia User Guide.Google Scholar
- 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. Google ScholarDigital Library
- Roth, S., and Black, M. J. 2005. Fields of experts: A framework for learning image priors. In CVPR. Google ScholarDigital Library
- Salomon, D. 2005. Coding for Data and Computer Communications, 1 ed. Springer. Google ScholarDigital Library
- 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.Google Scholar
- Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In ICCV, 839--846. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Weiss, Y., and Freeman, B. 2007. What makes a good model of natural images. In CVPR.Google Scholar
Index Terms
- Noise brush: interactive high quality image-noise separation
Recommendations
Noise brush: interactive high quality image-noise separation
SIGGRAPH Asia '09: ACM SIGGRAPH Asia 2009 papersThis 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