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

Bart Goossens; Jan Aelterman; Hiep Luong; Aleksandra Pizurica; Wilfried Philips

doi:10.1117/12.2008537

14 February 2013 Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain

Bart Goossens, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, Wilfried Philips

Author Affiliations +

Proceedings Volume 8657, Computational Imaging XI; 865704 (2013) https://doi.org/10.1117/12.2008537
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

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. 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.

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Bart Goossens, Jan Aelterman, Hiep Luong, Aleksandra Pizurica, and Wilfried Philips "Bayesian demosaicing using Gaussian scale mixture priors with local adaptivity in the dual tree complex wavelet packet transform domain", Proc. SPIE 8657, Computational Imaging XI, 865704 (14 February 2013); https://doi.org/10.1117/12.2008537

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KEYWORDS

Denoising

Wavelets

RGB color model

Phase modulation

Image resolution

Optical filters

Digital cameras

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