Abstract
Digital cameras are equipped with passive autofocus mechanisms where a lens is focused using only the camera’s optical system and an algorithm for controlling the lens. The speed and accuracy of the autofocus algorithm are crucial to user satisfaction. In this paper, we address the problems of identifying the global optimum and significant local optima (or peaks) when focusing an image. We show that supervised machine learning techniques can be used to construct a passive autofocus heuristic for these problems that out-performs an existing hand-crafted heuristic and other baseline methods. In our approach, training and test data were produced using an offline simulation on a suite of 25 benchmarks and correctly labeled in a semi-automated manner. A decision tree learning algorithm was then used to induce an autofocus heuristic from the data. The automatically constructed machine-learning-based (ml-based) heuristic was compared against a previously proposed hand-crafted heuristic for autofocusing and other baseline methods. In our experiments, the ml-based heuristic had improved speed—reducing the number of iterations needed to focus by 37.9 % on average in common photography settings and 22.9 % on average in a more difficult focus stacking setting—while maintaining accuracy.
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Notes
The software and data are available at: https://cs.uwaterloo.ca/~vanbeek/research.
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This work was supported in part by a University of Waterloo President’s Scholarship of Distinction, an NSERC USRA award, and an NSERC Discovery Grant.
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Mir, H., Xu, P., Chen, R. et al. An autofocus heuristic for digital cameras based on supervised machine learning . J Heuristics 21, 599–616 (2015). https://doi.org/10.1007/s10732-015-9291-4
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DOI: https://doi.org/10.1007/s10732-015-9291-4