Reconstruction of objects from incomplete or corrupted tomographic data remains a fundamental challenge in inverse problems. In particular, the Broken Ray Transform (BRT) leads to highly ill-posed reconstruction tasks when ray paths are limited, noisy, or partially missing due to geometric or physical constraints. This study investigates robust reconstruction methods for BRT under three experimental conditions: (1) noisy full-angle data, (2) limited-angle acquisition, and (3) incomplete boundary measurements with missing ray segments. We analyze the performance of three reconstruction approaches—Filtered Back-Projection (FBP), Tikhonov-regularized minimization, and Total Variation (TV) regularization—across these scenarios. Quantitative evaluations using RMSE, PSNR, and SSIM reveal that regularization-based methods significantly outperform classical FBP under noise and angular limitations. TV produces the highest structural fidelity, especially for sharp-edge phantoms, while Tikhonov demonstrates superior noise suppression. The results indicate that properly tuned variational regularization substantially improves stability and accuracy of BRT reconstruction under practical constraints and limited measurement capabilities.

Broken Ray Transform, inverse problems, limited-angle tomography

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