Self-Optimization of Camera Hardware
Abstract: This thesis aims to investigate the automatic tuning of hardware parameters in a camera's image processing pipeline. In order to solve the tuning problem, it is formulated as a black-box optimization problem centered around a physical camera unit. Optimization is performed by comparing the camera's output to a reference image. Several black-box optimization algorithms were tested: Bayesian Optimization, Evolutionary Optimization, Particle Swarm Optimization, Simulated Annealing, DIRECT, and Rowan's Subplex Method. Results indicate that it is feasible to automatically tune camera hardware parameters using black-box optimization algorithms. For 14 parameters, Rowan's Subplex Method performs best with an average error of 6.25. When optimizing a much larger set of 71 parameters, Simulated Annealing, Evolutionary, and Rowan's Subplex Method perform best with an average error of 9.77, 17.92, and 18.05 respectively.
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