Similarity measure is an essential component in image registration. In this article, we propose a novel similarity measure for registration of two or more images. The proposed method is motivated by the fact that optimally registered images can be sparsified hierarchically in the gradient domain and frequency domain with the separation of sparse errors. One of the key advantages of the proposed similarity measure is its robustness in dealing with severe intensity distortions, which widely exist on medical images, remotely sensed images and natural photos due to differences of acquisition modalities or illumination conditions. Two efficient algorithms are proposed to solve the batch image registration and pair registration problems in a unified framework. We have validated our method on extensive and challenging data sets. The experimental results demonstrate the robustness, accuracy and efficiency of our method over nine traditional and state-of-the-art algorithms on synthetic images and a wide range of real-world applications.

中文翻译：

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鲁棒图像配准的分层稀疏表示


相似性度量是图像配准的重要组成部分。在本文中，我们提出了一种用于注册两个或更多图像的新颖相似性度量。所提出的方法的动机是，最佳配准图像可以在梯度域和频域中分层稀疏，并分离稀疏误差。所提出的相似性度量的关键优点之一是其在处理严重强度失真方面的鲁棒性，由于采集方式或照明条件的差异，这种强度失真广泛存在于医学图像、遥感图像和自然照片中。提出了两种有效的算法来解决统一框架中的批量图像配准和配对配准问题。我们已经在广泛且具有挑战性的数据集上验证了我们的方法。实验结果证明了我们的方法在合成图像和广泛的实际应用中超过九种传统和最先进的算法的鲁棒性、准确性和效率。