Learning to Autofocus
| Autor: | Richard Strong Bowen, Qiurui He, Charles Herrmann, Jonathan T. Barron, Ramin Zabih, Neal Wadhwa, Rahul Garg |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Autofocus
FOS: Computer and information sciences business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Ordinal regression law.invention Task (project management) Lens (optics) law 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Artificial intelligence Baseline (configuration management) business computer |
| Zdroj: | CVPR |
| Popis: | 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. CVPR 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|