arXiv:2504.07758cs.CVeess.IV2025-04CVPR被引 20

一次完成去马赛克与超分辨率,提升偏振图像质量与参数准确性。

PIDSR: Complementary Polarized Image Demosaicing and Super-Resolution

  • 设计联合框架,同时解决偏振图像去马赛克与超分辨率问题。
  • 在合成与真实数据上均达到当前最优性能,显著降低DoP和AoP误差。
  • 适合需要高精度偏振信息的视觉任务,如材料检测、自动驾驶。

偏振相机可在单次拍摄中捕捉不同偏振角度的多幅图像,为基于偏振的下游任务带来便利。然而,其直接输出为彩色-偏振滤波阵列(CPFA)原始图像,需经去马赛克重建全分辨率、全色彩的偏振图像;此过程常引入伪影,导致偏振相关参数(如偏振度DoP、偏振角AoP)出现误差。此外,受硬件限制,偏振相机分辨率通常远低于常规RGB相机。现有偏振图像去马赛克(PID)方法无法提升分辨率,而偏振图像超分辨率(PISR)方法虽能从去马赛克结果生成高分辨率(HR)图像,却往往保留甚至放大去马赛克引入的误差。本文提出PIDSR,一种联合框架,可直接从CPFA原始图像中鲁棒地恢复高质量高分辨率偏振图像,并获得更准确的DoP与AoP。实验表明,所提方法在合成与真实数据上均达到领先性能,并有效提升下游任务表现。

原文摘要 · Abstract (English)

Polarization cameras can capture multiple polarized images with different polarizer angles in a single shot, bringing convenience to polarization-based downstream tasks. However, their direct outputs are color-polarization filter array (CPFA) raw images, requiring demosaicing to reconstruct full-resolution, full-color polarized images; unfortunately, this necessary step introduces artifacts that make polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP) prone to error. Besides, limited by the hardware design, the resolution of a polarization camera is often much lower than that of a conventional RGB camera. Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution, while polarized image super-resolution (PISR) methods, though designed to obtain high-resolution (HR) polarized images from the demosaicing results, tend to retain or even amplify errors in the DoP and AoP introduced by demosaicing artifacts. In this paper, we propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution, showing the ability to robustly obtain high-quality HR polarized images with more accurate DoP and AoP from a CPFA raw image in a direct manner. Experiments show our PIDSR not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks.

偏振成像图像重建超分辨率深度学习

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