Advanced search
1 file | 973.22 KB

Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain

Bart Goossens (UGent) , Jan Aelterman (UGent) , Hiep Luong (UGent) , Aleksandra Pizurica (UGent) and Wilfried Philips (UGent)
Author
Organization
Abstract
In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. ABSTRACT In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU.
Keywords
complex wavelets, Keywords: demosaicing, wavelet denoising, Bayer pattern

Downloads

  • SPIE EI2013 Goossens.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 973.22 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Goossens, Bart, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, and Wilfried Philips. 2013. “Bayesian Demosaicing Using Gaussian Scale Mixture Priors with Local Adaptivity in the Dual Tree Complex Wavelet Packet Transform Domain.” In Proceedings of SPIE, the International Society for Optical Engineering, ed. CA Bouman, I Pollak, and PJ Wolfe. Vol. 8657. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering.
APA
Goossens, B., Aelterman, J., Luong, H., Pizurica, A., & Philips, W. (2013). Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain. In C. Bouman, I. Pollak, & P. Wolfe (Eds.), Proceedings of SPIE, the International Society for Optical Engineering (Vol. 8657). Presented at the Conference on Computational Imaging XI ; IS&T SPIE Electronic Imaging 2013, Bellingham, WA, USA: SPIE, the International Society for Optical Engineering.
Vancouver
1.
Goossens B, Aelterman J, Luong H, Pizurica A, Philips W. Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain. In: Bouman C, Pollak I, Wolfe P, editors. Proceedings of SPIE, the International Society for Optical Engineering. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering; 2013.
MLA
Goossens, Bart et al. “Bayesian Demosaicing Using Gaussian Scale Mixture Priors with Local Adaptivity in the Dual Tree Complex Wavelet Packet Transform Domain.” Proceedings of SPIE, the International Society for Optical Engineering. Ed. CA Bouman, I Pollak, & PJ Wolfe. Vol. 8657. Bellingham, WA, USA: SPIE, the International Society for Optical Engineering, 2013. Print.
@inproceedings{3152290,
  abstract     = {In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. ABSTRACT In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU.},
  articleno    = {865704},
  author       = {Goossens, Bart and Aelterman, Jan and Luong, Hiep and Pizurica, Aleksandra and Philips, Wilfried},
  booktitle    = {Proceedings of SPIE, the International Society for Optical Engineering},
  editor       = {Bouman, CA and Pollak, I and Wolfe, PJ},
  isbn         = {9780819494306},
  issn         = {0277-786X},
  keywords     = {complex wavelets,Keywords: demosaicing,wavelet denoising,Bayer pattern},
  language     = {eng},
  location     = {Burlingame, CA, USA},
  pages        = {8},
  publisher    = {SPIE, the International Society for Optical Engineering},
  title        = {Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain},
  url          = {http://dx.doi.org/10.1117/12.2008537},
  volume       = {8657},
  year         = {2013},
}

Altmetric
View in Altmetric
Web of Science
Times cited: