Fourier Single-Pixel Imaging Method Based on Adaptive Sampling of Spectral Features
The improvement of imaging efficiency in Fourier single-pixel imaging(FSI)is mainly achieved with the help of optimized reconstruction algorithms and optimized sampling methods.However,with a limited number of samplings,FSI cannot accurately sample critical frequencies,resulting in poor imaging quality.To solve this problem,a strategy for adaptive sampling of spectral features is proposed.First,the degree of concentration of energy in the Fourier domain is investigated as a way to determine the optimal radius of low-frequency equidistant pre-sampling,and further,the corresponding Fourier coefficients are measured by means of pre-sampling the low-frequency components to estimate the key spectral positions,which ultimately realizes the image reconstruction.Compared with the adaptive sampling method based on energy continuity in the high-frequency direction,this method can adaptively select better sampling paths for different spectral feature targets,obtain the key Fourier coefficients,and then improve the imaging quality,with a peak signal-to-noise ratio increase of 2.28 dB and a structural similarity increase of 15.83%.Therefore,this method has the advantage of efficient spatial information acquisition in response to FSI of unknown feature targets,and is expected to be applied in single-pixel fast real-time imaging.
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