Accelerated fixed-point iterative reconstruction for fiber borescope imaging
Abstract
Computational imaging systems with embedded processing have potential advantages in power consumption, computing speed, and cost. However, common processors in embedded vision systems have limited computing capacity and low level of parallelism. The widely used iterative algorithms for image reconstruction rely on floating-point processors to ensure calculation precision, which require more computing resources than fixed-point processors. Here we present a regularized Landweber fixed-point iterative solver for image reconstruction, implemented on a field programmable gated array (FPGA). Compared with floating-point embedded uniprocessors, iterative solvers implemented on the fixed-point FPGA gain 1 to 2 orders of magnitude acceleration, while achieving the same reconstruction accuracy in comparable number of effective iterations. Specifically, we have demonstrated the proposed fixed-point iterative solver in fiber borescope image reconstruction, successfully correcting the artifacts introduced by the lenses and fiber bundle.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 30, 2023
- Source ID
- 10.1364/oe.495252
Entities
People
- Andrew B. Klein
- Dewan Saiham
- Shuo Pang
- Zheyuan Zhu
Organizations
- Army Research Office
- National Aeronautics and Space Administration
- National Science Foundation
- University of Central Florida