提出首个极化去噪与去马赛克联合数据集与基准方法
Polarization Denoising and Demosaicking: Dataset and Baseline Method
- 采用先去噪后去马赛克的信号处理流程
- 在40个真实场景下验证，噪声抑制效果优于现有方法
- 适合极化成像、低光视觉等研究者参考
平面分割式（DoFP）偏振相机可在单次曝光中获取多个偏振方向图像，对偏振信息应用具有重要价值。其图像处理流程包含去噪和去马赛克两个关键步骤。尽管无噪声情况下的偏振去马赛克已受关注，但因缺乏合适的评估数据集和可靠基线方法，联合去噪与去马赛克的研究仍较匮乏。本文提出一种新数据集与方法。数据集包含40个真实场景及三种噪声水平，提供成对的噪声马赛克输入与无噪声全分辨率图像。方法采用先去噪后去马赛克的信号处理范式，基于公认组件实现可复现性。实验表明，该方法在图像重建性能上优于其他替代方案，为该任务提供了坚实基线。
原文摘要 · Abstract (English)
A division-of-focal-plane (DoFP) polarimeter enables us to acquire images with multiple polarization orientations in one shot and thus it is valuable for many applications using polarimetric information. The image processing pipeline for a DoFP polarimeter entails two crucial tasks: denoising and demosaicking. While polarization demosaicking for a noise-free case has increasingly been studied, the research for the joint task of polarization denoising and demosaicking is scarce due to the lack of a suitable evaluation dataset and a solid baseline method. In this paper, we propose a novel dataset and method for polarization denoising and demosaicking. Our dataset contains 40 real-world scenes and three noise-level conditions, consisting of pairs of noisy mosaic inputs and noise-free full images. Our method takes a denoising-then-demosaicking approach based on well-accepted signal processing components to offer a reproducible method. Experimental results demonstrate that our method exhibits higher image reconstruction performance than other alternative methods, offering a solid baseline.
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