用深度图作实部，提升彩色图像修复质量。
Depth-Aided Color Image Inpainting in Quaternion Domain
- 将深度信息作为四元数实部，融合颜色与深度相关性
- 修复精度和视觉质量均优于传统方法，提升明显
- 适合需要高精度图像修复的场景，如自动驾驶
本文提出一种基于深度辅助的彩色图像修复方法——深度辅助低秩四元数矩阵补全（D-LRQMC）。传统四元数图像修复中，颜色通道被表示为三个虚部，实部设为零且无信息。本方法将深度信息作为四元数表示的实部，利用颜色与深度间的相关性提升修复效果。首先使用传统LRQMC恢复图像并估计深度；随后将估计深度作为实部重新构建四元数矩阵，并再次执行LRQMC。仿真结果表明，相比传统LRQMC，D-LRQMC在多种图像上显著提升了重建精度与视觉质量，验证了深度信息在四元数域中用于彩色图像处理的有效性。
原文摘要 · Abstract (English)
In this paper, we propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC). In conventional quaternion-based inpainting techniques, the color image is expressed as a quaternion matrix by using the three imaginary parts as the color channels, whereas the real part is set to zero and has no information. Our approach incorporates depth information as the real part of the quaternion representations, leveraging the correlation between color and depth to improve the result of inpainting. In the proposed method, we first restore the observed image with the conventional LRQMC and estimate the depth of the restored result. We then incorporate the estimated depth into the real part of the observed image and perform LRQMC again. Simulation results demonstrate that the proposed D-LRQMC can improve restoration accuracy and visual quality for various images compared to the conventional LRQMC. These results suggest the effectiveness of the depth information for color image processing in quaternion domain.
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