Anisetti Marco, Damiani Ernesto
IEEE Trans Image Process. 2016 Nov;25(11):5369-5382. doi: 10.1109/TIP.2016.2604489. Epub 2016 Aug 30.
Demosaicking is a digital image process to reconstruct full color digital images from incomplete color samples from an image sensor. It is an unavoidable process for many devices incorporating camera sensor (e.g., mobile phones, tablet, and so on). In this paper, we introduce a new demosaicking algorithm based on polynomial interpolation-based demosaicking. Our method makes three contributions: calculation of error predictors, edge classification based on color differences, and a refinement stage using a weighted sum strategy. Our new predictors are generated on the basis of on the polynomial interpolation, and can be used as a sound alternative to other predictors obtained by bilinear or Laplacian interpolation. In this paper, we show how our predictors can be combined according to the proposed edge classifier. After populating three color channels, a refinement stage is applied to enhance the image quality and reduce demosaicking artifacts. Our experimental results show that the proposed method substantially improves over the existing demosaicking methods in terms of objective performance (CPSNR, S-CIELAB ΔE*, and FSIM), and visual performance.
去马赛克是一种数字图像处理方法，用于从图像传感器的不完整颜色样本中重建全彩色数字图像。对于许多包含相机传感器的设备（如手机、平板电脑等）来说，这是一个不可避免的过程。在本文中，我们介绍了一种基于多项式插值的去马赛克新算法。我们的方法有三个贡献：误差预测器的计算、基于颜色差异的边缘分类以及使用加权和策略的细化阶段。我们的新预测器是基于多项式插值生成的，可以作为通过双线性或拉普拉斯插值获得的其他预测器的合理替代。在本文中，我们展示了如何根据提出的边缘分类器组合我们的预测器。在填充三个颜色通道后，应用细化阶段来提高图像质量并减少去马赛克伪像。我们的实验结果表明，所提出的方法在客观性能（CPSNR、S-CIELAB ΔE* 和 FSIM）和视觉性能方面比现有的去马赛克方法有显著改进。
