Loading...
Please wait, while we are loading the content...
Similar Documents
Depth Estimation for Integral Imaging Microscopy Using a 3D–2D CNN with a Weighted Median Filter
| Content Provider | MDPI |
|---|---|
| Author | Kwon, Ki-Chul Hossain, Biddut Alam, Shahinur Jeon, Seok-Hee Kim, Nam Imtiaz, Shariar |
| Copyright Year | 2022 |
| Description | This study proposes a robust depth map framework based on a convolutional neural network (CNN) to calculate disparities using multi-direction epipolar plane images (EPIs). A combination of three-dimensional (3D) and two-dimensional (2D) CNN-based deep learning networks is used to extract the features from each input stream separately. The 3D convolutional blocks are adapted according to the disparity of different directions of epipolar images, and 2D-CNNs are employed to minimize data loss. Finally, the multi-stream networks are merged to restore the depth information. A fully convolutional approach is scalable, which can handle any size of input and is less prone to overfitting. However, there is some noise in the direction of the edge. A weighted median filtering (WMF) is used to acquire the boundary information and improve the accuracy of the results to overcome this issue. Experimental results indicate that the suggested deep learning network architecture outperforms other architectures in terms of depth estimation accuracy. |
| Starting Page | 5288 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22145288 |
| Journal | Sensors |
| Issue Number | 14 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-15 |
| Access Restriction | Open |
| Subject Keyword | Sensors Remote Sensing Depth Estimation Integral Imaging Microscopy Light-filed Microscopy Deep Learning Machine Intelligence 3d Convolutional Neural Network |
| Content Type | Text |
| Resource Type | Article |
