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首页> 外文会议>Computational imaging XI >Bayesian Demosaicing using Gaussian Scale Mixture Priors with Local Adaptivity in the Dual Tree Complex Wavelet Packet Transform Domain
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Bayesian Demosaicing using Gaussian Scale Mixture Priors with Local Adaptivity in the Dual Tree Complex Wavelet Packet Transform Domain

机译：在双树复数小波包变换域中使用具有局部适应性的高斯尺度混合先验的贝叶斯去马赛克

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摘要

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.
机译：在数码相机和移动电话中，一直存在增加图像分辨率，减小传感器尺寸并使用较短曝光时间的趋势。由于较小的传感器会固有地导致更多的噪声和更差的空间分辨率，因此需要数字后处理技术来解决许多伪像。彩色滤光片阵列（CFA）使用彩色滤光片的交替图案，由于价格和功耗原因，它们非常受欢迎。但是，滤色器阵列需要使用后处理技术（例如去马赛克）来恢复全分辨率RGB图像。最近，人们对联合执行去马赛克和去噪的技术产生了兴趣。这具有以下优点：对于所考虑的噪声模型，可以最佳地（例如，在MSE意义上）执行去马赛克和去噪，同时避免在顺序使用去马赛克和去噪时引入的伪像。在本文中，我们将继续基于小波的去马赛克技术的研究。这些方法在计算上很简单，非常适合与降噪组合。因此，我们将在考虑局部适应性的情况下，导出复杂小波包域中的贝叶斯最小平方误差（MMSE）联合去马赛克和去噪规则。作为图像模型，我们将使用高斯比例混合，从而利用复数子波的方向性。我们的结果表明，该技术能够以较低的计算成本，在消除所有噪声的同时，重构图像中的精细细节。特别是，在最近的中端GPU上，一个12兆像素RAW图像的完整重建（包括色彩校正，白平衡等）需要3.5秒。

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  • 来源
    《Computational imaging XI》|2013年|865704.1-865704.8|共8页
  • 会议地点 Burlingame CA(US)
  • 作者

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

  • 作者单位

    Ghent University, Dept. of Telecommunications and Information Processing, TELIN-IPI-iMinds, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;

    Ghent University, Dept. of Telecommunications and Information Processing, TELIN-IPI-iMinds, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;

    Ghent University, Dept. of Telecommunications and Information Processing, TELIN-IPI-iMinds, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;

    Ghent University, Dept. of Telecommunications and Information Processing, TELIN-IPI-iMinds, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;

    Ghent University, Dept. of Telecommunications and Information Processing, TELIN-IPI-iMinds, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    demosaicing; Bayer pattern; complex wavelets; wavelet denoising;

    机译：去马赛克拜耳模式复杂小波小波去噪;

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