由于采用了更为先进的成像技术，光场相机可以同步获取场景的空间信息与角度信息。该技术以牺牲空间分辨率为代价，实现了更高维度的场景表示。为了提高光场相机拍摄场景的空间分辨率，本文搭建了角度差异强化的光场超分辨率重构网络。该网络先采用8个多分支残差块实现浅层特征提取，再采用4个强化的角度可变形对准模块实现深层特征提取，最后采用6个简化的残差特征蒸馏模块和像素洗牌模块完成数据重构。所提网络在利用光场角度差异完成空间信息超分时，更加强调视图自身特征的深入挖掘，以获得更加丰富的视图间差异特征。在5组公开的光场数据集上对本文所提网络的性能进行了验证，本文算法获得了PSNR、SSIM值更高的高分辨率光场子孔径图像。

The experiments are carried out from the aspects of validity verification of each network module, comparison of subjective visual effects, comparison of quantitative evaluation results, and algorithm complexity. The performance of the proposed network is verified on five public light field data sets. The proposed algorithm obtains high-resolution light field sub-aperture images with higher PSNR and SSIM.Based on the advanced imaging technology, light field camera can obtain the spatial information and the angular information of the scene synchronously. It achieves higher dimensional scene representation by sacrificing the spatial resolution. In order to improve the spatial resolution of the light field image, a light field super-resolution reconstruction network based on angle difference enhancement is built in this paper. In the proposed network, eight multi-branch residual blocks are used to extract shallow features. Then, four enhanced angular deformable alignment modules are used to extract deep features. Finally six simplified residual feature distillation modules and pixel shuffle modules are used to complete data reconstruction. The proposed network takes advantage of the angle difference of the light field to complete the spatial information super-resolution. In order to obtain more features difference between different views, the own feature of the single view is emphasized during the feature extraction. The performance of the proposed network is verified on five public light field data sets. The proposed algorithm obtains high-resolution light field sub-aperture images with higher PSNR and SSIM.