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http://dx.doi.org/10.3837/tiis.2019.05.016

No-reference Sharpness Index for Scanning Electron Microscopy Images Based on Dark Channel Prior  

Li, Qiaoyue (School of Information and Control Engineering, China University of Mining and Technology)
Li, Leida (School of Information and Control Engineering, China University of Mining and Technology)
Lu, Zhaolin (School of Information and Control Engineering, China University of Mining and Technology)
Zhou, Yu (School of Information and Control Engineering, China University of Mining and Technology)
Zhu, Hancheng (School of Information and Control Engineering, China University of Mining and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.5, 2019 , pp. 2529-2543 More about this Journal
Abstract
Scanning electron microscopy (SEM) image can link with the microscopic world through reflecting interaction between electrons and materials. The SEM images are easily subject to blurring distortions during the imaging process. Inspired by the fact that dark channel prior captures the changes to blurred SEM images caused by the blur process, we propose a method to evaluate the SEM images sharpness based on the dark channel prior. A SEM image database is first established with mean opinion score collected as ground truth. For the quality assessment of the SEM image, the dark channel map is generated. Since blurring is typically characterized by the spread of edge, edge of dark channel map is extracted. Then noise is removed by an edge-preserving filter. Finally, the maximum gradient and the average gradient of image are combined to generate the final sharpness score. The experimental results on the SEM blurred image database show that the proposed algorithm outperforms both the existing state-of-the-art image sharpness metrics and the general-purpose no-reference quality metrics.
Keywords
Image quality assessment; No-reference; Sharpness; Dark channel; Scanning electron microscopy;
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