Pixel-Level Non-Local Method-Based Depth Image Inpainting - Xiaoya Dai; Yingkun Hou; Shuqi Zhang; Hao Hou; Bin Feng; Tao Lin; Mengyu Liu (IEEE Access)
Each pixel intensity value in a depth image represents the depth information in the scene. However, during the generation of depth images, some important information may be lost in the form of pieces, which can seriously impact subsequent applications, such as computer vision. Existing methods attempt to apply traditional image restoration methods for filling the lost areas in depth images have proven ineffective in inpainting the structural information in the scene. In this paper, we propose a depth image inpainting method based on pixel-level non-local method. In order to effectively inpaint the depth images, we add 0.7 density salt-and-pepper noise to the original depth image in advance, we then implement image block-matching on the noise-added depth image to obtain some similar image block groups, and then scan each image block to form a column vector. We further stack all vectors to construct a two-dimensional matrix. Finally, we implement row-matching on the constructed matrix to obtain many similar pixel groups. The separable Haar transform is implemented on each similar pixel groups, hard-thresholding on transformed coefficients can effectively remove the added noise and bring the original neighbor pixels in the depth image into the information lost area, thus, the lost information can be effectively inpainted. Inverse Haar transform, row-aggregation and block-aggregation are successfully implemented to complete the depth image inpainting. Experimental results show that the proposed method can achieve higher PSNR values than existing methods on public datasets, and the subjective visual quality of the inpainted images is also satisfactory.