SSIM vs. Noise level: Reconstruction quality comparison using limited projection data from 0 to , , , and .

SSIM vs. Noise level: Reconstruction quality comparison using limited projection data from 0 to , , , and .

<p>SSIM vs. Noise level: Reconstruction quality comparison using limited projection data from 0 to , , , and .</p>

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Bibliographic Details
Main Author:	Haytham A. Ali (21703201) (author)
Other Authors:	Essam A. Rashed (11949249) (author), Hiroyuki Kudo (8688534) (author)
Published:	2025
Subjects:	Cell Biology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unlike previous methods introducing varying levels incomplete angular coverage basis functions composed conditions often result challenging imaging conditions quality reconstructions due method &# 8217 dice coefficients demonstrate novel reconstruction framework image reconstruction problem based image reconstruction parametric level set multiscale gaussian functions integrates compressed sensing insufficient projection data div >< p projection data compressed sensing reconstruction quality gaussian coefficients angle conditions method tailored level values level images gaussian noise represent boundaries promoting sparsity paper addresses noisy scenarios highly undersampled discrete tomography discrete images ct ). constructed using computed tomography boundary accuracy
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