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    • 摘要: 基于双平面模型的四维光场表示形式,光场相机以牺牲图像空间分辨率为代价,实现了三维场景空间信息和角度信息的同步记录。为了提高光场图像的空间分辨率,本文搭建了基于双路引导更新机制的光场图像超分辨率重建网络。网络前端以不同形式的图像阵列为输入,构建残差串并联卷积实现了空间、角度信息解耦合。针对解耦合后的空间、角度信息,设计了双路引导更新模块,采用逐级增强、融合、再增强的方式,完成空间信息与角度信息的交互引导迭代更新。最后将逐级更新后的角度信息送入简化后的残差特征蒸馏模块,实现数据重建。对比实验表明,所提网络在有效控制复杂度的基础上,获得了更好的超分性能。

       

      Abstract: Based on the four-dimensional representation of the two-plane model, the light field camera captures spatial and angular information of the three-dimensional scene simultaneously at the expense of image spatial resolution. To improve the spatial resolution of light field images, a two-way guided updating network for light field image super-resolution is built in this work. In the front of the network, different forms of image arrays are used as inputs, and the residual series and parallel convolution are constructed to realize the decoupling of spatial and angular information. Aiming at the decoupled spatial information and angular information, a two-way guide updating module is designed, which adopts step-by-step enhancement, fusion, and re-enhancement methods to complete the interactive guidance iterative update of spatial and angular information. Finally, the step-by-step updated angular information is sent to the simplified residual feature distillation module to realize data reconstruction. Many experimental results have confirmed that our proposed method achieves state-of-the-art performance while effectively controlling complexity.