通过纹理感知先验提升复杂光照下图像分解质量
Texture-aware Intrinsic Image Decomposition with Model- and Learning-based Priors
- 引入纹理引导正则项，区分材质纹理与光照影响
- 在真实图像上实现更清晰的反射层与阴影层分离
- 适合需要高质量图像分解的应用场景
本文旨在从单张图像中恢复固有反射层和阴影层。尽管这一内在图像分解问题已研究数十年，但在复杂场景下（如空间变化的光照和丰富纹理）仍具挑战性。本文提出一种新方法，有效处理严重光照变化和丰富纹理，可生成高质量的内在图像。我们观察到，以往基于学习的方法常产生无纹理且过度平滑的内在图像，这些特征可用于推断光照与纹理信息。基于此，我们设计了纹理引导正则项，并将其融入优化框架，以分离材料纹理与光照效应。实验表明，结合新型纹理感知先验可显著优于现有方法。
原文摘要 · Abstract (English)
This paper aims to recover the intrinsic reflectance layer and shading layer given a single image. Though this intrinsic image decomposition problem has been studied for decades, it remains a significant challenge in cases of complex scenes, i.e. spatially-varying lighting effect and rich textures. In this paper, we propose a novel method for handling severe lighting and rich textures in intrinsic image decomposition, which enables to produce high-quality intrinsic images for real-world images. Specifically, we observe that previous learning-based methods tend to produce texture-less and over-smoothing intrinsic images, which can be used to infer the lighting and texture information given a RGB image. In this way, we design a texture-guided regularization term and formulate the decomposition problem into an optimization framework, to separate the material textures and lighting effect. We demonstrate that combining the novel texture-aware prior can produce superior results to existing approaches.
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。