论文标题
基于DINOISER的2D超分辨率多参考的预测
Denoiser-based projections for 2-D super-resolution multi-reference alignment
论文作者
论文摘要
我们研究了2-D超分辨率多参考比对（SR-MRA）问题：从其下采样，循环翻译和嘈杂的副本中估算图像。 SR-MRA问题是生物分子结构确定问题的数学抽象。由于SR-MRA问题在没有任何先验知识的情况下被置于不足之处，因此准确的图像估计依赖于设计良好描述感兴趣图像的统计数据的先验。在这项工作中，我们以图像处理的最新进展为基础，并利用Denoisers作为图像先验的力量。特别是，我们建议将Denoisiser用作预测，并设计两个计算框架来估计图像：预期的期望最大化和矩的预测方法。我们提供了有效的GPU实现，并通过广泛的数值实验在广泛的参数和图像上进行了大量的数值实验来证明这些算法的有效性。
We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies. The SR-MRA problem serves as a mathematical abstraction of the structure determination problem for biological molecules. Since the SR-MRA problem is ill-posed without prior knowledge, accurate image estimation relies on designing priors that well-describe the statistics of the images of interest. In this work, we build on recent advances in image processing, and harness the power of denoisers as priors of images. In particular, we suggest to use denoisers as projections, and design two computational frameworks to estimate the image: projected expectation-maximization and projected method of moments. We provide an efficient GPU implementation, and demonstrate the effectiveness of these algorithms by extensive numerical experiments on a wide range of parameters and images.
