On the use of deep learning for computational imaging
Singapore-MIT Alliance for Research and Technology · Massachusetts Institute of Technology · +4 more institutions
Abstract
Since their inception in the 1930–1960s, the research disciplines of computational imaging and machine learning have followed parallel tracks and, during the last two decades, experienced explosive growth drawing on similar progress in mathematical optimization and computing hardware. While these developments have always been to the benefit of image interpretation and machine vision, only recently has it become evident that machine learning architectures, and deep neural networks in particular, can be effective for computational image formation, aside from interpretation. The deep learning approach has proven to be especially attractive when the measurement is noisy and the measurement operator ill posed or…
Citation impact
- FWCI
- 49.44
- Percentile
- 100%
- References
- 286
Authors
3- GBGeorge BarbastathisCorresponding
Singapore-MIT Alliance for Research and Technology, Massachusetts Institute of Technology
- AÖAydogan Özcan
University of California, Los Angeles
- GSGuohai Situ
Chinese Academy of Sciences, Shanghai Institute of Optics and Fine Mechanics, University of Chinese Academy of Sciences
Topics & keywords
- Artificial intelligence
- Deep learning
- Computer science
- Machine learning
- Field (mathematics)
- Process (computing)
- Interpretation (philosophy)
- Imaging science