边缘区域约束的导向滤波深度像超分辨率重建算法

为了解决TOF(Time of Flight)相机获取的深度像分辨率较低的问题,基于导向滤波器提出了一种边缘区域约束的超分辨率重建算法。首先对低分辨深度像进行初始上采样,利用多尺度边缘检测提取深度像的边缘区域;然后根据同场景中灰度图像与深度像的边缘相似性,提取公共边缘区域;最后,根据灰度图像的边缘像素在公共边缘区域中的位置约束导向滤波器的系数生成,重新对导向滤波器的系数进行加权,从而构建出高分辨率的深度图。通过标准数据库Middlebury数据集进行验证,与3种近年来基于滤波的超分辨重建算法相比较,文中方法既能有效地保护重建深度像的边缘结构,同时具有较高的计算效率。研究结果可以为低分辨激光成像雷达的目标识别、场景重建等对实时性要求较高的工程应用提供理论依据。

Edge area constraint guided filter depth image super-resolution reconstruction algorithm

A super-resolution reconstruction method was proposed to solve the edge blurring and texture copying in the depth map from the super-resolution process when using guided filter. The proposed method was based on the guide filter and high-resolution grey image’s edge feature-constrained. Firstly, up-sampling the low resolution depth image by interpolation and the edge region of the depth image was extracted by multi-scale edge detection. Subsequently, the depth map and high-resolution grey image’s edge were extracted. Then, the public edge region was extracted according to the similarity between gray image and depth map. Finally, the high-resolution depth map was constructed through the position of gray image edge pixels in the public edge region constrainting the feneration of guide filter coefficients. By means of the validation of Middlebury data set and the combination with four super-resolution reconstrcution algorithms based on the filter, the proposed method can better protect the edge feature of depth map reconstruted by super-resolution, attain the high-resolution depth, and has high calculation efficiency. The results can provide theoretical basis for target recognition and scene reconstruction of low resolution lidar.