Removing Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing
Removing Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing
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DOI：
10.1017/s1431927619000254
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发表时间：
2019-06-01
影响因子：
2.8
通讯作者：
Hovden, Robert
中科院分区：
文献类型：
--
作者：
Schwartz, Jonathan;Jiang, Yi;Hovden, Robert
高方向性的图像伪影，如离子磨帘幕，机械划痕，或图像条纹从光束不稳定性降低显微照片的可解释性。这些不需要的非周期性特征使图像沿着主方向延伸，并占据傅立叶空间中的小楔形信息。删除此楔形数据将用更复杂的条纹和模糊伪影（在断层扫描界称为缺失楔形伪影）替换条纹、划痕或帘幕。在这里，我们克服了这个问题，通过恢复丢失的区域使用总变差最小化，它利用图像稀疏为基础的重建技术-俗称为压缩感知（CS）-可靠地恢复损坏的条纹状功能的图像。我们的方法消除了束流不稳定性、离子磨帘幕、机械划痕或任何条纹特征，并在低信噪比下保持稳健。这种方法的成功是通过利用CS无法恢复傅立叶空间中高度局部化和缺失的定向结构来实现的。
Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs. These unwanted, aperiodic features extend the image along a primary direction and occupy a small wedge of information in Fourier space. Deleting this wedge of data replaces stripes, scratches, or curtaining, with more complex streaking and blurring artifacts-known within the tomography community as missing wedge artifacts. Here, we overcome this problem by recovering the missing region using total variation minimization, which leverages image sparsity-based reconstruction techniques-colloquially referred to as compressed sensing (CS)-to reliably restore images corrupted by stripe-like features. Our approach removes beam instability, ion mill curtaining, mechanical scratches, or any stripe features and remains robust at low signal-to-noise. The success of this approach is achieved by exploiting CS's inability to recover directional structures that are highly localized and missing in Fourier Space.