通过多尺度交互网络提升双目图像超分辨率效果
Multi-scale interaction network for stereo image super-resolution
- 设计多尺度空间-通道注意力模块增强单视图特征提取
- 引入双视图对极线注意力实现更精准的视图间匹配
- 在多个数据集上超越多数现有方法，适合双目视觉研究者
双目图像超分辨率旨在利用双目系统的互补信息生成高分辨率图像。尽管以往研究已取得显著成果，但视图内与视图间信息的潜力尚未被充分挖掘。为此，我们提出一种新颖的多尺度交互网络用于双目图像超分辨率。具体而言，设计了多尺度空间-通道注意力模块，结合多尺度大可分离核注意力与简单通道注意力，以提升视图内特征提取能力；同时提出双视图对极线注意力模块，采用最优传输算法实现沿对极线更精确的匹配。大量实验与消融分析表明，所提方法在多个基准上表现优异，优于大多数现有最先进方法。
原文摘要 · Abstract (English)
Stereo image super-resolution aims to generate high-resolution images by leveraging complementary information from binocular systems. Although previous studies have achieved impressive results, the potential of intra-view and cross-view information has not been fully exploited. To address this issue, we propose a novel multi-scale interaction network for stereo image super-resolution. Specifically, we design a Multi-scale Spatial-Channel Attention Module that utilizes multi-scale large separable kernel attention and simple channel attention to improve intra-view feature extraction. Additionally, we propose a Dual-View Epipolar Attention Module, utilizing an optimal transport algorithm to achieve more accurate matching along the epipolar line. Extensive experimental and ablation studies show that our method achieves competitive results that outperform most SOTA methods.
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