Details

Autor(en) / Beteiligte

• Fu, Congrui
• Ma, Xin
• Liu, Yao
• Hou, Junhui
• Yuan, Hui

Titel

DRLFNet: A Dense-Connection Residual Learning Neural Network for Light Field Super Resolution

Ist Teil von

• Image and Graphics, p.501-510

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Light field records both spatial and angular information of light rays. By using light field cameras, 3D scenes can be reconstructed easily for further virtual reality applications. Limited by the sensor size, there is a trade-off between the spatial and angular resolution. To address this problem, we propose a dense-connection residual learning neural network, namely DRLFNet, to super resolve light field images in spatial domain. The dense-connection residual learning is implemented based on the proposed dense-connection residual block (DResBlock) that is used to efficiently exploit the joint spatial and angular features and the hierarchical features in different layers. Experimental results demonstrate that the proposed method out-performs other state-of-the-art methods by a large margin in both visual and numerical evaluations.