Learning to Autofocus
This work addresses autofocus performance issues for digital camera users, representing an incremental improvement over existing methods.
The paper tackles the problem of autofocus in digital cameras by proposing a learning-based approach that uses a new dataset with per-pixel depths from multi-view stereo. The result is a significant improvement, reducing the mean absolute error by a factor of 3.6 compared to the best baseline algorithm.
Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance. We propose a learning-based approach to this problem, and provide a realistic dataset of sufficient size for effective learning. Our dataset is labeled with per-pixel depths obtained from multi-view stereo, following "Learning single camera depth estimation using dual-pixels". Using this dataset, we apply modern deep classification models and an ordinal regression loss to obtain an efficient learning-based autofocus technique. We demonstrate that our approach provides a significant improvement compared with previous learned and non-learned methods: our model reduces the mean absolute error by a factor of 3.6 over the best comparable baseline algorithm. Our dataset and code are publicly available.