用图模型初始化+可学习扰动，实现高效高质图像插值
Unrolling Graph-based Douglas-Rachford Algorithm for Image Interpolation with Informed Initialization
- 基于已知插值器构建图邻接矩阵，作为网络初始结构
- 通过狄克逊-拉舍弗迭代逐步优化，性能超越现有方法
- 轻量级设计适合低资源部署，结果可解释性强
传统深度神经网络随机初始化参数并用随机梯度下降优化，易陷入表现不佳的局部极小值。针对图像插值任务，本文利用最新理论将（伪）线性插值器Θ映射为满足图移变（GSV）先验的图滤波器，先根据已知插值器Θ初始化有向图邻接矩阵A，建立基准性能。为进一步提升效果，从数据中学习扰动矩阵P和P(2)以增强A，其恢复效果通过狄克逊-拉舍弗（DR）迭代逐步实现，并将该过程展开为轻量且可解释的神经网络。在多种图像插值场景下的实验表明，该方法达到当前最优性能，同时显著减少网络参数量与推理复杂度。
原文摘要 · Abstract (English)
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 image interpolation and leveraging a recent theorem that maps a (pseudo-)linear interpolator Θ to a directed graph filter that is a solution to a corresponding MAP problem with a graph shift variation (GSV) prior, we first initialize a directed graph adjacency matrix A given a known interpolator Θ, 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 progressively via Douglas-Rachford (DR) iterations, which we unroll into a lightweight and interpretable neural net. Experiments on different image interpolation scenarios demonstrate state-of-the-art performance, while drastically reducing network parameters and inference complexity.
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