论文标题
非规范采样传感器的四分之一采样蒙版的迭代优化
Iterative Optimization of Quarter Sampling Masks for Non-Regular Sampling Sensors
论文作者
论文摘要
非规范采样可以以噪声为代价降低别名。最近，已经表明，当其单个像素的表面部分覆盖时，可以使用常规的常规成像传感器进行非规范采样。该技术称为四分之一采样（也是1/4个采样），因为每个像素的四分之一对光敏感。为此，选择适当的采样面膜对于达到高重建质量至关重要。在这项工作的范围内，我们提出了一种迭代算法，以改善任意季度的采样面膜，从而导致重建质量不断提高。在重建算法方面，我们测试了两种简单的算法，即线性插值和最近的邻居插值，以及两个更复杂的算法，即，转向内核回归和频率选择性超元。除了我们优化的掩码引起的随机季度采样掩模相对于随机的季度采样掩模，除了+0.31 dB至+0.68 dB的PSNR增益外，视觉上明显的增强功能是可感知的。
Non-regular sampling can reduce aliasing at the expense of noise. Recently, it has been shown that non-regular sampling can be carried out using a conventional regular imaging sensor when the surface of its individual pixels is partially covered. This technique is called quarter sampling (also 1/4 sampling), since only one quarter of each pixel is sensitive to light. For this purpose, the choice of a proper sampling mask is crucial to achieve a high reconstruction quality. In the scope of this work, we present an iterative algorithm to improve an arbitrary quarter sampling mask which results in a continuous increase of the reconstruction quality. In terms of the reconstruction algorithms, we test two simple algorithms, namely, linear interpolation and nearest neighbor interpolation, as well as two more sophisticated algorithms, namely, steering kernel regression and frequency selective extrapolation. Besides PSNR gains of +0.31 dB to +0.68 dB relative to a random quarter sampling mask resulting from our optimized mask, visually noticeable enhancements are perceptible.
