Tahereh Bahraini
Papers
1
Total Citations
9
H-Index
1
About
Tahereh Bahraini is a researcher specializing in computational imaging and signal processing, with a particular focus on enhancing 3D data quality. Her most cited work, "Edge preserving range image smoothing using hybrid locally kernel-based weighted least square" (2022, 9 citations), introduces a novel hybrid approach that combines kernel-based weighting with least squares optimization to smooth range images while preserving sharp edges—a critical challenge in applications like autonomous navigation and 3D reconstruction. This contribution addresses a fundamental trade-off in image processing, offering a robust solution that maintains geometric fidelity without blurring structural boundaries. Bahraini’s research demonstrates a keen ability to bridge theoretical optimization techniques with practical sensor data challenges, making her work relevant for fields requiring precise depth perception. While her citation count reflects an emerging career, the technical depth of her approach signals potential for broader impact as 3D sensing technologies continue to advance. Her work stands as a valuable resource for students and researchers exploring edge-aware filtering in range imaging.
Research Focus
Key Achievements
Top Papers
- 1