标题： 基于 Douglas-Rachford 迭代的图像插值图算法展开 显示英文标题

标题： Graph Algorithm Unrolling with Douglas-Rachford Iterations for Image Interpolation with Guaranteed Initialization

摘要： 传统深度神经网络（DNNs）在随机初始化网络参数后，通过随机梯度下降（SGD）优化每个参数，导致出现性能较差的局部最小值的较大风险。专注于图像插值问题，并利用最近的一个定理，该定理将(pseudo-)线性插值器{\Theta }映射到一个定向图滤波器，该滤波器是通过图移位变化（GSV）先验正则化的最大后验（MAP）问题的解，我们首先基于已知的插值器{\Theta }初始化一个定向图邻接矩阵A，建立基准性能。然后，为了进一步提升性能，我们从数据中学习扰动矩阵P和P(2)来增强A，其恢复效果通过Douglas-Rachford（DR）迭代实现，我们将这些迭代展开成一个轻量级可解释的神经网络。实验结果表明，图像插值结果达到最先进水平，同时大幅减少了网络参数。 显示英文摘要

摘要： Conventional deep neural nets (DNNs) initialize network parameters at random and then optimize each one via stochastic gradient descent (SGD), resulting in substantial risk of poor-performing local minima.Focusing on the image interpolation problem and leveraging a recent theorem that maps a (pseudo-)linear interpolator {\Theta} to a directed graph filter that is a solution to a MAP problem regularized with a graph shift variation (GSV) prior, we first initialize a directed graph adjacency matrix A based on a known interpolator {\Theta}, establishing a baseline performance.Then, towards further gain, we learn perturbation matrices P and P(2) from data to augment A, whose restoration effects are implemented via Douglas-Rachford (DR) iterations, which we unroll into a lightweight interpretable neural net.Experimental results demonstrate state-of-the-art image interpolation results, while drastically reducing network parameters.