Abstract

We propose an encoder–decoder with densely convolutional networks model to recover the depth information from a single RGB image without the need for depth sensors. The encoder part serves to extract the most representative information from the original data through a series of convolution operations and to reduce the resolution of the spatial input feature. We use the decoder section to produce an upsampling structure that improves the output resolution. Our model is trained from scratch, without any special tuning process, and uses a new optimization function to adaptively learn the rate. We demonstrate the effectiveness of the method by evaluating both indoor and outdoor scenes, and the experimental results show that our proposed approach is more accurate than competing methods.

© 2019 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Monocular catadioptric panoramic depth estimation via caustics-based virtual scene transition

Yu He, Lingxue Wang, Yi Cai, and Wei Xue
J. Opt. Soc. Am. A 33(9) 1872-1879 (2016)

Focus prediction in digital holographic microscopy using deep convolutional neural networks

Tomi Pitkäaho, Aki Manninen, and Thomas J. Naughton
Appl. Opt. 58(5) A202-A208 (2019)

Convolutional neural networks for whole slide image superresolution

Lopamudra Mukherjee, Adib Keikhosravi, Dat Bui, and Kevin W. Eliceiri
Biomed. Opt. Express 9(11) 5368-5386 (2018)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (6)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (7)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription