Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/24103
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Shirani, Shahram | - |
dc.contributor.advisor | Wu, Xiaolin | - |
dc.contributor.author | Hassanisaadi, Hamed | - |
dc.date.accessioned | 2019-03-21T17:52:59Z | - |
dc.date.available | 2019-03-21T17:52:59Z | - |
dc.date.issued | 2018 | - |
dc.identifier.uri | http://hdl.handle.net/11375/24103 | - |
dc.description.abstract | We present a method that can increase the quality of a low-light stereo image. While traditional stereo imaging methods have focused on estimating depth from stereo images, our method utilizes stereo images to enhance the low-light condition. The critical challenge for enhancing the low-light condition of stereo images is the disparity between the left and the right images. We proposed an end-to-end convolutional neural network to enhance the low-light condition in stereo images without estimating the disparity. Our proposed network has two sub-networks: the rst network learns how to enhance the low-light condition of stereo images in luminance, and the second network learns how to reconstruct a normal-light full-color image from enhanced luminance and chrominance of the input image. Our two-stage joint network enhances the low-light condition of stereo images significantly more than single-image low-light enhancement method. | en_US |
dc.language.iso | en | en_US |
dc.title | Low-light Stereo Image Enhancement Using Convolutional Neural Network | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Doctor of Philosophy (PhD) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
thesis_HamedHassanisaadi_400048983.pdf | 16.36 MB | Adobe PDF | View/Open |
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.