多网格反投影超分辨率和深度滤波可视化
Multigrid Backprojection Super-Resolution and Deep Filter Visualization
摘要：
我们介绍了一种新的深度学习架构，用于基于原始高分辨率图像的示例进行大因子（例如4x，8x）的图像放大。我们的目标是重建高分辨率的图像，从他们的缩小版本。所提出的系统执行多级渐进式放大，从小因子（2x）开始，并针对更高因子（4x和8x）进行更新。该系统是递归的，因为它在每一级重复相同的过程。它也是残差，因为我们使用网络来更新经典升频器的输出。通过在卷积网络的特征中计算的迭代反投影（IBP）来改进网络残差。工作在多个层次上，我们扩展了标准的反投影算法，使用递归类似于多网格算法通常用作大型线性方程组的求解器。最后，我们展示了如何将该网络解释为标准的上采样和滤波放大器，并具有适应几何形状的空变滤波器。这种方法使我们能够可视化网络如何学习升级。最后，我们的系统达到了最先进的质量与相对较少的参数数量的模型。
英文摘要：
We introduce a novel deep-learning architecture for image upscaling by large factors (e.g. 4x, 8x) based on examples of pristine high-resolution images. Our target is to reconstruct high-resolution images from their downscale versions. The proposed system performs a multi-level progressive upscaling, starting from small factors (2x) and updating for higher factors (4x and 8x). The system is recursive as it repeats the same procedure at each level. It is also residual since we use the network to update the outputs of a classic upscaler. The network residuals are improved by Iterative Back-Projections (IBP) computed in the features of a convolutional network. To work in multiple levels we extend the standard back-projection algorithm using a recursion analogous to Multi-Grid algorithms commonly used as solvers of large systems of linear equations. We finally show how the network can be interpreted as a standard upsampling-and-filter upscaler with a space-variant filter that adapts to the geometry. This approach allows us to visualize how the network learns to upscale. Finally, our system reaches state of the art quality for models with relatively few number of parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
