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.