Laser & Optoelectronics Progress, Volume. 55, Issue 12, 121004(2018)
Dense Disparity Map Extraction Method Based on Improved Convolutional Neural Network
According to the problem of the severe detail loss of the disparity map generated by the current convolutional neural network methods, a structural improvement method is proposed. The 4 layers convolutional structure of the feature extraction part from original network is added to 7 layers to maximize the accuracy. And, the proposed dual pyramid structure is introduced to the network to combine the multi-scale down-sampling information with the feature information, which keeps the details of the original input images. Experimental results show that the error rate of the improved network reduces from 3.029% to 2.795%, and the generated disparity maps have better connectivity.
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Dongzhen Huang, Qin Zhao, Huawei Liu, Baoqing Li, Xiaobing Yuan. Dense Disparity Map Extraction Method Based on Improved Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121004
Category: Image Processing
Received: May. 4, 2018
Accepted: Jun. 8, 2018
Published Online: Aug. 1, 2019
The Author Email: Yuan Xiaobing (sinowsn@mail.sim.ac.cn)