| Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Outline of Final Research Achievements |
Recently, Compressed Sensing (CS) has been successfully applied to image reconstruction in medical x-ray CT and Transmission Electron Microscopy (TEM). In this research project, we propose a new mathematical framework of CS named as Super Compressed Sensing, which significantly improves the performances of CS with respect to image quality. The key of Super CS is to use the nonlinear filter called Nonlocal Mean Filter to evaluate signal sparsity, which leads to preserving complicated intensity changes in images such as image textures and image gradations. We applied the Super CS to image reconstruction in medical CT and TEM. The results demonstrate that the Super CS significantly outperforms the ordinary CS in terms of image quality.
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