Color-Guided Depth Image Recovery With Adaptive Data Fidelity and Transferred Graph Laplacian Regularization

Depth images play an important role and are prevalently used in many computer vision and computational imaging tasks. However, due to the limitation of active sensing technology, the captured depth images in practice usually suffer from low resolution and noise, which prevents its further applications. To remedy this problem, in this paper, we first propose an adaptive data fidelity formulation to optimally generate each depth pixel from a mixture probability distribution, characterizing the similarity both in the depth map and the corresponding high-resolution guided color image. The proposed method is able to fit the distribution of the input depth signal as an optimization problem by maximizing the mixture probability. Furthermore, to promote the piecewise property that depth images exhibit, we propose a transferred graph Laplacian model as a regularization term, which is general and able to handle various depth recovery tasks such as super-resolution and denoising well. Specifically, each pixel within the recovered depth image is represented as a vertex in a graph with weights in connected edges representing the similarity between vertices. By minimizing the squared variations of the image signal, the task of depth image recovery can be converted to the problem of graph-based image filtering. Since the proposed graph Laplacian regularization model is able to fully exploit a priori information about the depth image, a much more accurate and robust estimation of the underlying depth can be obtained. Extensive experiment evaluations verify that the proposed method obtains recovered depth with higher quality in terms of both objective and subjective criteria, compared with most of the state-of-the-art methods.

I. Introduction

As an indication of true position in 3D space, depth image plays an important role in a variety of real-world applications, such as object reconstruction, immersive visual communication, 3D television and entertainment. Although the well-developed 3D imaging hardware makes it more accurate and affordable to acquire depth information, there is still a big gap between the resolution of depth image and that of its corresponding color image, hindering its further applications. Moreover, due to the limitations of the current depth sensing technologies, acquired depth images are often corrupted by sensor noises or surface reflection, which further limits practical applications based on depth information. Therefore, depth image recovery becomes a vital problem in the development of real-world applications, for example 3D reconstruction [1], human-computer interaction (HCI) [2], [3] and depth-based image rendering (DIBR) [4], [5].