Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
This addresses depth image inpainting for applications like robotics or 3D reconstruction, but it is incremental as it builds on existing low rank methods with a domain-specific tweak.
The paper tackles depth image inpainting by proposing a low gradient regularization method that reduces penalties for gradient 1 to allow gradual depth changes, integrated with low rank regularization, and shows effectiveness in experiments.
We consider the case of inpainting single depth images. Without corresponding color images, previous or next frames, depth image inpainting is quite challenging. One natural solution is to regard the image as a matrix and adopt the low rank regularization just as inpainting color images. However, the low rank assumption does not make full use of the properties of depth images. A shallow observation may inspire us to penalize the non-zero gradients by sparse gradient regularization. However, statistics show that though most pixels have zero gradients, there is still a non-ignorable part of pixels whose gradients are equal to 1. Based on this specific property of depth images , we propose a low gradient regularization method in which we reduce the penalty for gradient 1 while penalizing the non-zero gradients to allow for gradual depth changes. The proposed low gradient regularization is integrated with the low rank regularization into the low rank low gradient approach for depth image inpainting. We compare our proposed low gradient regularization with sparse gradient regularization. The experimental results show the effectiveness of our proposed approach.