.png){kind=icon}
.png){kind=icon}
Return
Pixel super-resolution using compressive sensing for interferometric quantitative phase imaging
.png){kind=icon}
PRE
AI
DOI：10.1016/j.optlaseng.2025.109045.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Interferometric quantitative phase microscopy (iQPM) with high resolution holds great potential for efficient label-free investigation of biological systems. However, due to the limited spatial bandwidth product, there trade-off between the field of view (FOV) and spatial resolution, which hinders the applications of iQPM large-scale in situ cellular phenotyping. To address this issue, we developed CS-iQPM, a method applying compressive sensing to capture sub-pixel signals in an iQPM image. In CS-iQPM, we first propose an efficient easy-to-implement frequency ordering sampling scheme to reorder the Hadamard basis, according to which mask was selected. This scheme outperforms random sampling as evaluated by three key metrices: peak to-noise ratio, root mean square error, and structural similarity. We second integrated the iterative thresholding algorithm with pseudo-inverse matrix to improve the efficiency and structural similarity reconstruction. Applying these two techniques to our iQPM, the CS-iQPM enhanced the pixel resolution about 4 pixel/mu m2 using 10x objective lens to 64 pixel/mu m2 within 2 s. Experimental results demonstrated CS-iQPM significantly enhanced the resolution of fine grid patterns and cells in quantitative phase images range larger than 500 mu m. We envision that CS-iQPM could be applied to many high-throughput in situ cellular phenotyping applications, such as measuring cellular morphologies or cellular mechanical properties at single-cell level in a monolayer during cell migrating.
Keywords:
Interferometric quantitative phase microscope
Compressive sensing
Pixel super resolution
Journal
IF:
3.7
Papers:
7.2K
Citations:
1.7W
Organization
No organization information available