Fast acquisition and reconstruction of optical coherence tomography images via sparse representation.
Fast acquisition and reconstruction of optical coherence tomography images via sparse representation.
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DOI：
10.1109/tmi.2013.2271904
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发表时间：
2013-11
影响因子：
10.6
通讯作者：
Farsiu S
中科院分区：
文献类型：
--
作者：
Fang L;Li S;McNabb RP;Nie Q;Kuo AN;Toth CA;Izatt JA;Farsiu S
在本文中，我们提出了一种新的技术，基于压缩感知的原则，重建和增强的多维图像数据。我们的方法是一个重大的改进和推广的多尺度稀疏基于层析去噪（MSBTD）算法，我们最近推出的减少斑点噪声。我们的新技术表现出几个优势MSBTD，包括它的能力，同时降低噪声和插值丢失的数据。与MSBTD不同，我们的新方法不需要来自目标成像对象的先验高质量图像，从而提供了缩短临床成像会话的可能性。这种新的图像恢复方法，我们称之为基于稀疏性的同时去噪和插值（SBSDI），利用稀疏表示字典构建从以前收集的数据集。我们测试了SBSDI算法的视网膜光谱域光学相干断层扫描图像中捕获的临床。实验表明，SBSDI算法在定性和定量上都优于其他最先进的方法。
In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.