Xin Liu

Dr. Xin Liu

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 August 2023 in Photonics
Verified
Journal Article
Open access
PDF file
XLXin Liu
Xin Liu
LLLang Liu
Lang Liu
JHJianheng Huang
Jianheng Huang
YLYaohu Lei
Yaohu Lei
JLJi Li
Ji Li
In the field of X-ray phase-contrast imaging, a time-saving approach and preservation of details are crucial factors for obtaining phase-contrast images. In this manuscript, a single grating imaging system is proposed to perform the X-ray phase-contrast imaging. Instead of the time-consuming phase-stepping method, this system uses a single-shot algorithm to retrieve the distribution of samples’ attenuation and phase gradient. Unlike the single-shot Fourier transform algorithm, which truncates the high-frequency component of the image and reduces the spatial resolution, our method can retrieve the attenuation and phase information images with the same spatial resolution as the images acquired directly by the X-ray detector used. Furthermore, by using a large-size X-ray detector (29 cm × 23 cm), the imaging system can be configured as either a microscopic instrument or a normal large field-of-view imaging system. Finally, a series of experiments were performed to validate the feasibility of the proposed method.

1 February 2024 in Optical Engineering
Verified
Journal Article
Closed access
PDF file
YLYaohu Lei
Yaohu Lei
SLSenlong Luo
Senlong Luo
LLLang Liu
Lang Liu
JHJianheng Huang
Jianheng Huang
XLXin Liu
Xin Liu
Single-shot non-invasive imaging through a scattering medium has been an area of research focus for several years. Achieving an accurate autocorrelation distribution is crucial for effective image reconstruction. However, the obtained autocorrelation is always contaminated by various types of noise. In this study, we propose an effective method to extract the autocorrelation distribution of the sample from the noise-laden raw data before image retrieval. This is accomplished by incorporating an intensity threshold and a spatial Gaussian filter into the retrieval algorithm, which effectively eliminates the noise artifacts in the reconstructed image. The proposed algorithm exhibited remarkable robustness for both visible (laser, white light, and light-emitting diode light) and X-ray light sources, and its efficacy was verified through multiple experiments.