MarsQE: 基于语义的压缩火星图像质量增强
MarsQE: Semantic-Informed Quality Enhancement for Compressed Martian Image
AI总结：
MarsQE通过基于语义的两阶段方法提升火星压缩图像质量，解决地球图像方法在火星场景下的不足。
摘要：
在火星探索任务中，由于地球与火星之间的带宽有限，有损图像压缩是必不可少的。然而，压缩可能会引入视觉伪影，使火星表面的地质分析变得更加复杂。现有的质量增强方法主要针对地球图像设计，在考虑火星图像独特语义方面存在不足。为应对这一挑战，我们深入分析了火星图像，基于语义得出两个关键见解：纹理相似性和在火星图像中纹理表示的紧凑性。受这些发现的启发，我们引入了MarsQE，一种创新的、基于语义的、两阶段质量增强方法，专门针对火星图像设计。第一阶段涉及基于语义的纹理相似参考图像匹配，第二阶段通过将这些参考图像的纹理模式转移到压缩图像上来增强图像质量。我们还开发了一个后增强网络，进一步减少压缩伪影并实现更优的压缩质量。我们的广泛实验表明，MarsQE在地球图像上显著优于现有方法，为火星图像的质量增强建立了新的基准。
英文摘要：
Lossy image compression is essential for Mars exploration missions, due to the limited bandwidth between Earth and Mars. However, the compression may introduce visual artifacts that complicate the geological analysis of the Martian surface. Existing quality enhancement approaches, primarily designed for Earth images, fall short for Martian images due to a lack of consideration for the unique Martian semantics. In response to this challenge, we conduct an in-depth analysis of Martian images, yielding two key insights based on semantics: the presence of texture similarities and the compact nature of texture representations in Martian images. Inspired by these findings, we introduce MarsQE, an innovative, semantic-informed, two-phase quality enhancement approach specifically designed for Martian images. The first phase involves the semantic-based matching of texture-similar reference images, and the second phase enhances image quality by transferring texture patterns from these reference images to the compressed image. We also develop a post-enhancement network to further reduce compression artifacts and achieve superior compression quality. Our extensive experiments demonstrate that MarsQE significantly outperforms existing approaches for Earth images, establishing a new benchmark for the quality enhancement on Martian images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
