E2SRLF: End-to-End Light Field Image Super-Resolution Depth Estimation

Light field camera usually sacrifice spatial resolution for increasing angular resolution. Although it can capture rich spatial and angular information, leads to the spatial resolution reduced greatly. However, existing super-resolution methods usually focus on spatial and angular super-resolution, but ignore the end-to-end disparity map super-resolution estimation. Meanwhile, recent depth estimation methods can not extract a higher resolution disparity map from the low-resolution light field images directly. Motivated by this issue, we propose an end-to-end light field image super-resolution depth estimation network, named E2SRLF. First, a multi-dimensional channel attention mechanism is introduced to reduce the influence of occlusion and weak textures, and enhance the learning of local and global feature. Then, a spatial super-resolution fusion upsampling method is proposed for constructing the super-resolution dimension and acquiring precise high-resolution information. Additionally, we introduce a high-low resolution collaborative constraint based loss function to enforce network training efficiency. Experimental results demonstrate that E2SRLF can generate a high accuracy high-resolution depth map from the low-resolution light field images directly. Comparing to most of the state-of-the-art light field image depth estimation methods, E2SRLF directly achieved more accuracy high resolution disparity map with the lower resolution light field images input. The code of our method are available at: https://github.com/sansi-zhang/E2SRLF.