Single-pixel imaging in the presence of specular reflections

Abstract

Single-pixel imaging (SPI), which uses a photodetector to detect the reflected total light intensity of a set of structured illumination patterns modulated by a target scene, provides a method for visible waveband imaging, hyperspectral imaging, and terahertz imaging. However, it faces a challenge when the scene to be imaged has specular reflections. To deal with this problem, a multi-angle method without feature matching is presented. With this method, the location of the detector does not affect image reconstruction, and the results of reconstruction at each location are matched at the pixel level automatically. In simulations, with the original image as a reference, the structural similarity index value of the picture obtained by the proposed method is 10% higher than the picture obtained from a single angle. The signal-to-noise ratio value of the picture obtained by the proposed method is 4.424, which is higher than 1.577 of the maximum value of the reconstruction result from a single angle. To evaluate the method, a metal key and an aircraft engine blade with specular reflections are taken as the target scene and are reconstructed from four different imaging perspectives, giving results that are matched at the pixel level. The final reconstructed image is obtained using the principal component analysis algorithm or the fourth-order partial differential equations and principal component analysis algorithm. Compared with the image obtained from a single angle, the correlation coefficient between the image obtained by the proposed method and the reference image is increased from the minimum value of 0.3139 to 0.7050, and the power ratio is increased from 4.52% to 73.63%. The proposed method has great potential specifically for improving the quality of SPI for scenes exhibiting specular reflections.

Large‐wavelength Gaussian deconvolution phase‐contrast computed tomography for THz continuous wave (0.11 THz)

“…To quantitatively evaluate the quality of the reconstruction images based on the unwrapped phase information, three reliable image quality assessment metrics such as root-mean-square error (RMSE), 34 the peak signal-to-noise ratio (PSNR) 35 and the structure similarity (SSIM) 36 are calculated as follows. where p r and p s are the pixel values from reconstruction images and standard images respectively, μ r and μ s are the local means, σ r and σ s are the standard deviations, σ r s , is the cross-covariance for p r and p s , N is the total number of pixels, c 1 and c 2 are constants that avoid the calculation error caused by the mean and standard deviation of 0.…”

“…To quantitatively evaluate the quality of the reconstruction images based on the unwrapped phase information, three reliable image quality assessment metrics such as root‐mean‐square error (RMSE), 34 the peak signal‐to‐noise ratio (PSNR) 35 and the structure similarity (SSIM) 36 are calculated as follows. $$$\mathrm{RMSE}=\sqrt{\frac{1}{N}\sum _{i=1}^{N}{({p}_{r}-{p}_{s})}^{2}},$$$ $$$\mathrm{PSNR}=20lg\frac{255}{\mathrm{RMSE}},$$$ $$$\mathrm{SSIM}=\frac{(2{\mu }_{r}{\mu }_{s}+{c}_{1})(2{\sigma }_{r,s}+{c}_{2})}{\left({\mu }_{r}^{2}+{\mu }_{s}^{2}+{c}_{1}\right)\left({\sigma }_{r}^{2}+{\sigma }_{s}^{2}+{c}_{2}\right)}$$$where $${p}_{r}$$ and $${p}_{s}$$ are the pixel values from reconstruction images and standard images respectively, $${\mu }_{r}$$ and $${\mu }_{s}$$ are the local means, $${\sigma }_{r}$$ and $${\sigma }_{s}$$ are the standard deviations, …”

Computational ghost imaging with adaptive intensity illumination for scenes featuring specular surfaces

“…The strong specular reflection component saturates certain pixels of the array sensor, resulting in the loss of spatial information in the reconstructed image. It also affects the reconstruction results of CGI [22]. Despite not saturating the single-pixel detector, it significantly compresses the dynamic range of the reconstructed image, rendering the shape information conveyed by the diffuse reflection component invisible.…”

“…However, precise pixel position alignment is required to combine corresponding pixels from each acquired image. The multi-flash [33] and multi-view [22,34] methods modify the flash direction or camera position to reduce the influence of specular reflection components, although they may not be suitable for imaging cylindrical specular surfaces.…”

“…However, it is crucial to conduct pixel matching between the DMD and the sensor, establishing their coordinate mapping and response curves before the imaging process. Besides, based on CGI, a multi-angle method without feature matching is proposed, but the method requires multiple detectors at different angles [22].…”