Image super-resolution (SR) techniques have been developing rapidly, benefiting from the invention of deep networks and its successive breakthroughs. However, it is acknowledged that deep learning and deep neural networks are difficult to interpret. SR networks inherit this mysterious nature and little works make attempt to understand them. In this paper, we perform attribution analysis of SR networks, which aims at finding the input pixels that strongly influence the SR results. We propose a novel attribution approach called local attribution map (LAM), which inherits the integral gradient method yet with two unique features. One is to use the blurred image as the baseline input, and the other is to adopt the progressive blurring function as the path function. Based on LAM, we show that: (1) SR networks with a wider range of involved input pixels could achieve better performance. (2) Attention networks and non-local networks extract features from a wider range of input pixels. (3) Comparing with the range that actually contributes, the receptive field is large enough for most deep networks. (4) For SR networks, textures with regular stripes or grids are more likely to be noticed, while complex semantics are difficult to utilize. Our work opens new directions for designing SR networks and interpreting low-level vision deep models.

本篇论文提出一种名为 LAM 的局部指向图方法，通过对超分辨率（SR）神经网络模型及图像的像素引入模糊因子，探索其对模型输出结果的影响，结果表明：1）输入像素范围越广，SR性能越好；2）采用更广范围的注意力机制及非局部卷积核能提取越多的特征；3）大多数深度网络具有足够大的感受野；4）相对于深度结构而言，具有规则线条或网格的纹理更易被模型识别和利用。

使用本地归属度映射解释超分辨网络