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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Laser Photon Rev. 2021 Jun 27;15(8):2100072. doi: 10.1002/lpor.202100072

Figure 7. Comparison between hybrid reconstruction method (pixel back projection followed by background suppression) and iterative-optimization-based methods on computational cost and reconstruction quality merit.

Figure 7.

(a-d) Comparison for object containing featureless discrete / isolated points, where particle clustering was used for background suppression. The computation was conducted on a workstation (Intel Xeon E5–2686 v4, 128 GB system RAM, MATLAB 2019b). (a) Computational cost. (b) Peak signal-to-noise ratio (PSNR) of the reconstruction. (c) Structure similarity index (SSIM) of the reconstruction. (d) Mean squared error of the reconstruction. (b)-(d) is compared to ground truth. (e-h) Same as (a-d), but for comparison for 3D object containing continuous features, where a convolutional neural network was used for background suppression. The computation was conducted on a workstation (2x Intel Xeon E5–2667 v3, 384 GB system RAM, MATLAB 2019b). The computational cost of the two optimization-based methods, ADMM and Richard-Lucy deconvolution increases rapidly with data scale, while it only increases modestly in our hybrid reconstruction method. When paired with background suppression algorithms, the pixel back projection method has a greatly improved reconstruction quality, similar as those from the two iterative-optimization-based methods.