提升火星车拍摄的立体图像质量，缓解压缩失真问题。
MarsSQE: Stereo Quality Enhancement for Martian Images Using Bi-level Cross-view Attention
- 利用双视角注意力机制挖掘火星图像间的关联性。
- 在自建数据集上实现显著视觉质量提升。
- 适合从事火星图像处理与遥感增强的研究者。
由于火星与地球间带宽有限，火星车拍摄的立体图像在传输前需经有损压缩，导致明显压缩伪影。本文提出一种名为MarsSQE的新方法，用于提升火星立体图像质量。首先，构建了首个火星立体图像数据集；通过该数据集分析发现，火星图像中跨视角相关性显著。基于此，设计了一种双层次交叉视角注意力网络，融合像素级注意力实现精准匹配和块级注意力获取更广上下文信息。实验表明，该方法能有效改善图像质量。
原文摘要 · Abstract (English)
Stereo images captured by Mars rovers are transmitted after lossy compression due to the limited bandwidth between Mars and Earth. Unfortunately, this process results in undesirable compression artifacts. In this paper, we present a novel stereo quality enhancement approach for Martian images, named MarsSQE. First, we establish the first dataset of stereo Martian images. Through extensive analysis of this dataset, we observe that cross-view correlations in Martian images are notably high. Leveraging this insight, we design a bi-level cross-view attention-based quality enhancement network that fully exploits these inherent cross-view correlations. Specifically, our network integrates pixel-level attention for precise matching and patch-level attention for broader contextual information. Experimental results demonstrate the effectiveness of our MarsSQE approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。