Hardie Russell
Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH 45469-0232, USA.
IEEE Trans Image Process. 2007 Dec;16(12):2953-64. doi: 10.1109/tip.2007.909416.
A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.
提出了一种使用自适应维纳滤波器的计算简单的超分辨率算法。该算法从具有重叠视场的低分辨率（LR）视频帧序列中生成分辨率提高的图像。该算法使用亚像素配准将每个LR像素值定位在一个公共空间网格上，该网格以输入帧的平均位置为参考。与一些先前的技术不同，LR像素的位置不会被量化到有限网格。输出的高分辨率（HR）像素是通过在局部移动窗口中对LR像素进行加权求和得到的。使用统计模型，为每个HR像素设计权重以最小化均方误差，并且它们取决于周围LR像素的相对位置。因此，这些权重在空间和时间上适应由于运动变化而导致的LR像素分布变化。这里考虑了全局和空间变化的统计模型。由于权重随LR像素的分布而自适应，因此它非常稳健，当观察到LR像素的不利分布时不会变得不稳定。对于平移运动，该算法具有低计算复杂度，并且很容易适用于实时和/或近实时处理应用。对于其他运动模型，计算复杂度会显著增加。然而，无论运动模型如何，该算法都适合并行实现。这里使用模拟和真实视频序列的一些实验结果证明了所提出算法的有效性。还进行了计算分析。
