Self-Optimization of Camera Hardware
(2021) EDAM05 20211Department of Computer Science
- 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.
... (More) - 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. (Less) - Popular Abstract
- When new cameras are developed, their image quality has to be tuned by expert engineers,
which normally takes several weeks.
Our thesis shows that tuning can be done automatically in a matter of minutes.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9057269
- author
- Kristoffersson Lind, Simon LU and Tykesson, Johannes
- supervisor
-
- Luigi Nardi LU
- organization
- alternative title
- Automatisk Optimering för KamerahÄrdvara
- course
- EDAM05 20211
- year
- 2021
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Optimization, Black-box optimization, Evolutionary, Bayesian, Simulated Annealing, DIRECT, Particle Swarm, Simplex
- report number
- 2021-23
- ISSN
- 1650-2884
- language
- English
- id
- 9057269
- date added to LUP
- 2021-10-11 09:15:50
- date last changed
- 2021-10-11 09:15:50
@misc{9057269, 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.}}, author = {{Kristoffersson Lind, Simon and Tykesson, Johannes}}, issn = {{1650-2884}}, language = {{eng}}, note = {{Student Paper}}, title = {{Self-Optimization of Camera Hardware}}, year = {{2021}}, }