Energy Efficient Approximate 3D Image Reconstruction

نویسندگان

چکیده

We demonstrate an efficient and accelerated parallel, sparse depth reconstruction framework using compressed sensing (compressed (CS)) approximate computing. Employing data parallelism for rapid image formation, the is reconstructed from sparsely sampled scenes convex optimization. Coupled with faster imaging, this sampling reduces significantly projected laser power in active systems such as light detection ranging (LiDAR) to allow eye safe operation at longer range. also how reduced precision leveraged reduce number of logic units field-programmable gate array (FPGA) implementations imaging systems. It enables significant reduction units, memory requirements consumption by over 80% minimal impact on quality reconstruction. To further accelerate processing, pre-computed, important components lower-upper (LU) decomposition other linear algebraic computations are used solve optimization problems. Our methodology demonstrated application alternating direction method multipliers (ADMM) proximal gradient descent (PGD) algorithms. For comparison, a fully discrete least square ( $d$ Sparse) presented. This demonstrates feasibility novel, high resolution, low frame rate LiDAR imagers based illumination use applications where resources strictly limited.

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ژورنال

عنوان ژورنال: IEEE Transactions on Emerging Topics in Computing

سال: 2022

ISSN: ['2168-6750', '2376-4562']

DOI: https://doi.org/10.1109/tetc.2021.3116471