Fundamental Limits in Multi-Image Alignment
Fundamental Limits in Multi-Image Alignment
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DOI：
10.1109/tsp.2016.2600517
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发表时间：
2016-11-01
影响因子：
5.4
通讯作者：
Sapiro, Guillermo
中科院分区：
文献类型：
--
作者：
Aguerrebere, Cecilia;Delbracio, Mauricio;Sapiro, Guillermo
多图像对齐的性能（将不同图像纳入一个坐标系）对于许多具有不同信噪比 (SNR) 条件的应用至关重要。人们投入了大量的精力来开发解决这个问题的方法。因此出现了几个重要的问题，包括：多图像对齐性能的基本限制是什么？访问更多图像是否可以改善对齐情况？理论界限为比较方法提供了基本基准，并有助于确定是否可以进行改进。在这项工作中，我们解决了当多个移位和噪声观测可用时寻找图像配准的性能限制的问题。我们推导并分析了基础图像的不同统计模型下的 Cramer-Rao 和 Ziv-Zakai 下界。我们根据输入图像的 SNR 条件给出的问题的难度级别来显示不同行为区域的存在。我们在此提出的分析进一步深入了解了多图像对齐问题的基本局限性。
The performance of multiimage alignment, bringing different images into one coordinate system, is critical in many applications with varied signal-to-noise ratio (SNR) conditions. A great amount of effort is being invested into developing methods to solve this problem. Several important questions thus arise, including: Which are the fundamental limits in multiimage alignment performance? Does having access to more images improve the alignment? Theoretical bounds provide a fundamental benchmark to compare methods and can help establish whether improvements can be made. In this work, we tackle the problem of finding the performance limits in image registration when multiple shifted and noisy observations are available. We derive and analyze the Cramer-Rao and Ziv-Zakai lower bounds under different statistical models for the underlying image. We show the existence of different behavior zones depending on the difficulty level of the problem, given by the SNR conditions of the input images. The analysis we present here brings further insight into the fundamental limitations of the multiimage alignment problem.