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Complex wavelet joint denoising and demosaicing using Gaussian scale mixtures

Bart Goossens (UGent) , Jan Aelterman (UGent) , Hiep Luong (UGent) , Aleksandra Pizurica (UGent) and Wilfried Philips (UGent)
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Abstract
Wavelet-based demosaicing techniques have the advantage of being computationally relatively fast, while having a reconstruction performance that is similar to state-of-the-art techniques. Because the demosaicing rules are linear, it is fairly simple to integrate denoising into the demosaicing. In this paper, we present a method that performs joint denoising and demosaicing, using a Gaussian Scale Mixture (GSM) prior model, thereby modeling the local edge direction as a hidden variable. The results indicate that this technique offers a better reconstruction performance (in PSNR sense and visually) than sequential demosaicing and denoising. On a recent GPU, our algorithm takes 3.5 s for reconstructing a 12 megapixel RAW digital camera image.
Keywords
Demosaicing, Complex wavelets, Bayer Pattern, Image denoising, IMAGES, DOMAIN, NOISE

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Chicago
Goossens, Bart, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, and Wilfried Philips. 2013. “Complex Wavelet Joint Denoising and Demosaicing Using Gaussian Scale Mixtures.” In IEEE International Conference on Image Processing ICIP, 445–448. New York, NY, USA: IEEE.
APA
Goossens, B., Aelterman, J., Luong, H., Pizurica, A., & Philips, W. (2013). Complex wavelet joint denoising and demosaicing using Gaussian scale mixtures. IEEE International Conference on Image Processing ICIP (pp. 445–448). Presented at the 20th IEEE International conference on Image Processing (ICIP 2013), New York, NY, USA: IEEE.
Vancouver
1.
Goossens B, Aelterman J, Luong H, Pizurica A, Philips W. Complex wavelet joint denoising and demosaicing using Gaussian scale mixtures. IEEE International Conference on Image Processing ICIP. New York, NY, USA: IEEE; 2013. p. 445–8.
MLA
Goossens, Bart, Jan Aelterman, Hiep Luong, et al. “Complex Wavelet Joint Denoising and Demosaicing Using Gaussian Scale Mixtures.” IEEE International Conference on Image Processing ICIP. New York, NY, USA: IEEE, 2013. 445–448. Print.
@inproceedings{4149455,
  abstract     = {Wavelet-based demosaicing techniques have the advantage of being computationally relatively fast, while having a reconstruction performance that is similar to state-of-the-art techniques. Because the demosaicing rules are linear, it is fairly simple to integrate denoising into the demosaicing. In this paper, we present a method that performs joint denoising and demosaicing, using a Gaussian Scale Mixture (GSM) prior model, thereby modeling the local edge direction as a hidden variable. The results indicate that this technique offers a better reconstruction performance (in PSNR sense and visually) than sequential demosaicing and denoising. On a recent GPU, our algorithm takes 3.5 s for reconstructing a 12 megapixel RAW digital camera image.},
  author       = {Goossens, Bart and Aelterman, Jan and Luong, Hiep and Pizurica, Aleksandra and Philips, Wilfried},
  booktitle    = {IEEE International Conference on Image Processing ICIP},
  isbn         = {9781479923410},
  issn         = {1522-4880},
  keyword      = {Demosaicing,Complex wavelets,Bayer Pattern,Image denoising,IMAGES,DOMAIN,NOISE},
  language     = {eng},
  location     = {Melbourne, VIC, Australia},
  pages        = {445--448},
  publisher    = {IEEE},
  title        = {Complex wavelet joint denoising and demosaicing using Gaussian scale mixtures},
  url          = {http://dx.doi.org/10.1109/ICIP.2013.6738092},
  year         = {2013},
}

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