Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/24103
Title: | Low-light Stereo Image Enhancement Using Convolutional Neural Network |
Authors: | Hassanisaadi, Hamed |
Advisor: | Shirani, Shahram Wu, Xiaolin |
Department: | Electrical and Computer Engineering |
Publication Date: | 2018 |
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. |
URI: | http://hdl.handle.net/11375/24103 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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thesis_HamedHassanisaadi_400048983.pdf | 16.36 MB | Adobe PDF | View/Open |
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