X-ray tomographic imaging has become an important analytical tool with a wide range of applications. It is inevitable that noise is introduced in CT images, and noise reduction is necessary. To solve this problem, we considered to use the nonlocal property of similar block search and proposed a deep learning network based on similar block learning for noise reduction of micro CT short exposure time scanned images to improve the scanning efficiency while ensuring high quality imaging. The method uses the output of the nonlocal method as a data preprocessing algorithm by combining a nonlocal block matching algorithm with a convolutional neural network, and uses a residual channel attention mechanism to learn the features after feature extraction, which reduces noise while preserving image details. Experimental results show that the method can remove noise from CT images quickly and effectively, and compared with the classical CPCE noise reduction method, the method improves the PSNR index by 1.52 dB, which is consistent with the theoretical assumption.
The alignment of the acquired projections is quite necessary for accurate reconstruction of nano computer tomography (nano CT) due to thermal drift. In this paper, a method based on features outlier elimination (OE) is proposed to reduce the drift artifacts from the reconstruction slices, and a series of reference sparse projections are required. The rough alignment is realized after the extraction from the Speeded Up Robust Features (SURF) of both the original projections and the reference projections, of which the structure similarity (SSIM) is utilized to eliminate the outlier features. Then, the rest features are used for the further alignment for reconstruction. The simulation results show that the proposed method is more accurate and robust than image registration method based on entropy correlation coefficient (ECC) and traditional SURF. Scanning results of bamboo stick show that the proposed method can preserve the details of slices.
A markerless projection drift alignment approach for X-ray nanotomography is presented. Drifts in projection from different angles are aligned by applying offsets calculated between successive images after acquisition time division, taking the advantage of the fact that the shorter the time, the less the drift. Involving neither iteration nor parameter selection, it can combine a number of existing image registration techniques and could be adopted for other tomographic imaging techniques. The application of this algorithm has been demonstrated in a laboratory X-ray nanotomography system using single photon detection, in which a standard Siemens star resolution target is initially captured for 2D evaluation and a bamboo stick is used for 3D imaging, leading to sharper image without blur and a much higher resolution.
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