分层联合双边滤波的深度图修复算法研究

Research on Depth Map Restoration Algorithm Based on Hierarchical Joint Bilateral Filter

三维场景建模及三维多目标检测识别等研究中需要获取高精度、高分辨率深度图,针对RGB-D传感器提供的深度信息存在分辨率低、深度值缺失和噪声干扰等问题,提出一种基于深度置信度的分层联合双边滤波深度图修复算法。基于深度信息获取存在的问题提出相应的深度退化模型,采用深度置信度测量对深度像素进行置信度分类,根据深度置信度确定滤波器窗口权重值,利用提出的分层联合双边滤波算法在待修复区域完成深度图修复。采用Middlebury标准数据库和自采数据库进行定性对比实验和定量结果分析表明,该算法对深度图修复后边缘更加清晰合理,消除了边缘模糊和纹理伪像,有效提高了三维深度图修复的精确度。

The depth map captured by RGB-D sensor usually has low resolution and lack of depth value,but three-dimensional multi-target detection and recognition need to obtain high-precision and high-resolution depth maps.This paper proposes a hierarchical joint bilateral filtering depth map restoration algorithm based on depth confidence.The depth degradation model based on the problem of the depth information capture device is proposed.The depth confidence measurement is used to classify the depth pixels,and the filter window weight value is determined according to the depth confidence.The hierarchical joint bilateral filtering is used to complete the restoration of hole region.The qualitative comparison and quantitative result analysis based on Middlebury standard database and the proposed database show that the edge of depth map is more clear and reasonable,and there is no edge blur and texture artifacts basically.Therefore,the experimental results show that the proposed algorithm can improve the accuracy of depth map restoration effectively.