Photoacoustic imaging using structured illumination and a single-pixel ultrasound detector

Raman microscopy provides chemically selective imaging by exploiting intrinsic vibrational properties of specimens. Yet, a fast acquisition, low phototoxicity, and non-specific (to a vibrational/electronic mode) super-resolution method has been elusive for tissue imaging. We demonstrate a single-pixel-based approach, combined with robust structured illumination, that enables fast super-resolution in stimulated Raman scattering microscopy at low power levels. The methodology is straightforward to implement and compatible with thick biological specimens, therefore paving the way for probing complex biological systems when exogenous labelling is challenging.

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Efficient Fourier Single-Pixel Imaging with Gaussian Random Sampling

Photonics ◽

10.3390/photonics8080319 ◽

2021 ◽

Vol 8 (8) ◽

pp. 319

Author(s):

Ziheng Qiu ◽

Xinyi Guo ◽

Tian’ao Lu ◽

Pan Qi ◽

Zibang Zhang ◽

...

Keyword(s):

Fourier Coefficients ◽

Fourier Spectrum ◽

Spatial Information ◽

Imaging Techniques ◽

Gaussian Function ◽

Sampling Strategy ◽

Acquisition Time ◽

Pixel Detector ◽

Structured Illumination ◽

Single Pixel

Fourier single-pixel imaging (FSI) is a branch of single-pixel imaging techniques. It allows any image to be reconstructed by acquiring its Fourier spectrum by using a single-pixel detector. FSI uses Fourier basis patterns for structured illumination or structured detection to acquire the Fourier spectrum of image. However, the spatial resolution of the reconstructed image mainly depends on the number of Fourier coefficients sampled. The reconstruction of a high-resolution image typically requires a number of Fourier coefficients to be sampled. Consequently, a large number of single-pixel measurements lead to a long data acquisition time, resulting in imaging of a dynamic scene challenging. Here we propose a new sampling strategy for FSI. It allows FSI to reconstruct a clear and sharp image with a reduced number of measurements. The key to the proposed sampling strategy is to perform a density-varying sampling in the Fourier space and, more importantly, the density with respect to the importance of Fourier coefficients is subject to a one-dimensional Gaussian function. The final image is reconstructed from the undersampled Fourier spectrum through compressive sensing. We experimentally demonstrate the proposed method is able to reconstruct a sharp and clear image of 256 × 256 pixels with a sampling ratio of 10%. The proposed method enables fast single-pixel imaging and provides a new approach for efficient spatial information acquisition.

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