Improving the accuracy and low-light performance of contrast-based autofocus using supervised machine learning

• We present a supervised machine learning approach to constructing an autofocus algorithm.
• We compare our automatically learned autofocus algorithm to previously proposed hand-crafted autofocus algorithms.
• Our autofocus algorithm is more accurate, especially in the presence of noise, such as in low-light situations, which are difficult for cameras.

The passive autofocus mechanism is an essential feature of modern digital cameras and needs to be highly accurate to obtain quality images. In this paper, we address the problem of finding a lens position where the image is in focus. We show that supervised machine learning techniques can be used to construct heuristics for a hill-climbing approach for finding such positions that out-performs previously proposed approaches in accuracy and robustly handles scenes with multiple objects at different focus distances and low-light situations. We gather a suite of 32 benchmarks representative of common photography situations and label them in an automated manner. A decision tree learning algorithm is used to induce heuristics from the data and the heuristics are then integrated into a control algorithm. Our experimental evaluation shows improved accuracy over previous work from 91.5% to 98.5% in regular settings and from 70.3% to 94.0% in low-light.