论文标题
探索图像超级分辨率的多尺度特征传播和通信
Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution
论文作者
论文摘要
多尺度技术在各种计算机视觉任务中取得了巨大的成功。但是，尽管该技术已纳入现有作品中，但仍缺乏对图像超级分辨率多尺度卷积变体的全面研究。在这项工作中，我们提出了广泛使用的多尺度结构的统一配方。通过此框架，我们系统地探索了多尺度卷积的两个因素 - 特征传播和跨尺度通信。根据调查，我们提出了一个通用，有效的多尺度卷积单元 - 多尺度跨尺度股份重量卷积（MS $^3 $ -CONV）。广泛的实验表明，所提出的MS $^3 $ -CONV可以比标准卷积更高的参数和计算成本获得更好的SR性能。除了定量分析之外，我们还全面研究了视觉质量，这表明MS $^3 $ -Conv的行为更好地恢复了高频细节。
Multi-scale techniques have achieved great success in a wide range of computer vision tasks. However, while this technique is incorporated in existing works, there still lacks a comprehensive investigation on variants of multi-scale convolution in image super resolution. In this work, we present a unified formulation over widely-used multi-scale structures. With this framework, we systematically explore the two factors of multi-scale convolution -- feature propagation and cross-scale communication. Based on the investigation, we propose a generic and efficient multi-scale convolution unit -- Multi-Scale cross-Scale Share-weights convolution (MS$^3$-Conv). Extensive experiments demonstrate that the proposed MS$^3$-Conv can achieve better SR performance than the standard convolution with less parameters and computational cost. Beyond quantitative analysis, we comprehensively study the visual quality, which shows that MS$^3$-Conv behave better to recover high-frequency details.
