Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

Marcela Carvalho; Bertrand Le Saux; Pauline Trouvé-Peloux; Andrés Almansa; Frédéric Champagnat

doi:10.1007/978-3-030-11009-3_18

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Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

Marcela Carvalho Bertrand Le Saux Pauline Trouvé-Peloux Andrés Almansa Frédéric Champagnat

Year: 2019 Lecture notes in computer science Pages: 307-323 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-11009-3_18

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Keywords:

Computer science Artificial intelligence Focus (optics) Computer vision Ground truth Artificial neural network Deep learning Depth map Code (set theory) Image (mathematics) Pattern recognition (psychology)

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Image Processing Techniques and Applications

Physical Sciences → Engineering → Media Technology

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Advanced Image Processing Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Deep Depth from Defocus: How Can Defocus Blur Improve 3D Estimation Using Dense Neural Networks?

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