Mäkinen Ymir, Marchesini Stefano, Foi Alessandro
Tampere University, Finland.
SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA.
J Synchrotron Radiat. 2022 May 1;29(Pt 3):829-842. doi: 10.1107/S1600577522002739. Epub 2022 Apr 21.
X-ray micro-tomography systems often suffer from high levels of noise. In particular, severe ring artifacts are common in reconstructed images, caused by defects in the detector, calibration errors, and fluctuations producing streak noise in the raw sinogram data. Furthermore, the projections commonly contain high levels of Poissonian noise arising from the photon-counting detector. This work presents a 3-D multiscale framework for streak attenuation through a purposely designed collaborative filtering of correlated noise in volumetric data. A distinct multiscale denoising step for attenuation of the Poissonian noise is further proposed. By utilizing the volumetric structure of the projection data, the proposed fully automatic procedure offers improved feature preservation compared with 2-D denoising and avoids artifacts which arise from individual filtering of sinograms.
X射线显微断层扫描系统常常存在高噪声水平。特别是，严重的环形伪影在重建图像中很常见，这是由探测器缺陷、校准误差以及原始正弦图数据中产生条纹噪声的波动所导致的。此外，投影通常包含由光子计数探测器产生的高水平泊松噪声。这项工作提出了一个三维多尺度框架，通过对体数据中的相关噪声进行专门设计的协同滤波来衰减条纹。还进一步提出了一个用于衰减泊松噪声的独特多尺度去噪步骤。通过利用投影数据的体结构，与二维去噪相比，所提出的全自动程序能够更好地保留特征，并避免了因对正弦图进行单独滤波而产生的伪影。
