S2O-FSPI: Fourier single pixel imaging via sampling strategy optimization

Fourier single pixel imaging utilizes pre-programmed patterns for laser spatial distribution modulation to reconstruct intensity image of the target through reconstruction algorithms. The approach features non-locality and high anti-interference performance. However, Poor image quality is induced when the target of interest is occluded in Fourier single pixel imaging. To address the problem, a deep learning-based image inpainting algorithm is employed within Fourier single pixel imaging to reconstruct partially obscured targets with high quality. It applies a distance-based segmentation method to segment obscured regions and the target of interest. Additionally, it utilizes an image inpainting network that combines multi-scale sparse convolution and transformer architecture, along with a reconstruction network that integrates Channel Attention Mechanism and Attention Gate modules to reconstruct complete and clear intensity images of the target of interest. The proposed method significantly expands the application scenarios and improves the imaging quality of Fourier single pixel imaging. Simulation and real-world experimental results demonstrate that the proposed method exhibits the high inpainting and reconstruction capacity in the conditions of hard occlusion and down-sampling.

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Sparse single-pixel imaging via optimization in nonuniform sampling sparsity

Yan,

Li,

Zhan

et al. 2023

Opt. Lett.

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Reducing the imaging time while maintaining reconstruction accuracy remains challenging for single-pixel imaging. One cost-effective approach is nonuniform sparse sampling. The existing methods lack intuitive and intrinsic analysis in sparsity. The lack impedes our comprehension of the form’s adjustable range and may potentially limit our ability to identify an optimal distribution form within a confined adjustable range, consequently impacting the method’s overall performance. In this Letter, we report a sparse sampling method with a wide adjustable range and define a sparsity metric to guide the selection of sampling forms. Through a comprehensive analysis and discussion, we select a sampling form that yields satisfying accuracy. These works will make up for the existing methods’ lack of sparsity analysis and help adjust methods to accommodate different situations and needs.

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S2O-FSPI: Fourier single pixel imaging via sampling strategy optimization

Cited by 4 publications

References 37 publications

High-quality Fourier single-pixel imaging via a block-scanning approach

High-quality Fourier single-pixel imaging via a block-scanning approach

Partial hard occluded target reconstruction of Fourier single pixel imaging guided through range slice

Sparse single-pixel imaging via optimization in nonuniform sampling sparsity

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