A Depth Map Post-Processing Approach Based on Adaptive Random Wa

Received July 22, 2016, accepted August 12, 2016, date of publication August 26, 2016, date of current version September 28, 2016.

Digital Object Identifier 10.1109/ACCESS.2016.2603220

A Depth Map Post-Processing Approach Based

on Adaptive Random Walk With Restart

HOSSEIN JAVIDNIA, (Student Member, IEEE), AND PETER CORCORAN, (Fellow, IEEE)

Department of Electronic Engineering, College of Engineering, National University of Ireland, Galway SW4 794, Ireland

Corresponding author: H. Javidnia (h.javidnia1@nuigalway.ie)

This work was supported by the Strategic Partnership Program of Science Foundation Ireland (SFI) and co-funded by SFI and FotoNation

Ltd., on Next Generation Imaging for Smartphone and Embedded Platforms under Project 13/SPP/I2868.

introduced to remove the artifacts that cause distortion in the image. The proposed post-processing method is

then compared with the top eight algorithms in the Middlebury benchmark. To explore how well this method

is able to compete with more widely known techniques, a comparison is performed with Google’s new depth

map estimation method. The experimental results demonstrate the accuracy and efficiency of the proposed

post-processing method.

INDEX TERMS Stereo matching, depth map, accuracy, edge preserving.

I. INTRODUCTION

In 3D computer graphics a depth map is an image or image

channel that contains information relating to the distance

relations help to provide a richer representation of objects and

their environment, often leading to improvements in existing

recognition tasks, as well as enabling further applications

such as robotics. In recent years, many new economical

facilities, including time-of-flight [2], [3], structured light [4],

and the Kinect were introduced for depth determination from

stereo images. Kinect captures pairs of synchronized depth-

color images for a scene within a range of several meters.

However, the depth map cannot be used directly in scene

reconstruction because it has some deficiencies such as gaps

dent on the intensity and color values of the window W in

which P is located. The initial matching cost is pixel-wise

which is often noisy with minimum information in parts of

the image with smoother texture. Therefore using the cost of

the neighboring regions will assign the best depth value to

pixel P.

On the other hand global methods consider the overall

structure of the scene and smoothen the image and then try

to solve the cost optimization problem.

vation that despite the fact that many stereo correspon-

dences are highly ambiguous, some of them can be robustly

matched.

While the processing speed of the LIBELAS is quite fast,

minimization which as all other global methods contains

data and smoothness term. The main contribution in data

term in this algorithm is that, it is being approximated by a

color weighted correlation. Afterwards, the data term is being

refined in occluded regions by employing the hierarchical

loopy belief propagation algorithm.

CostFilter [9] is a framework for multiple applications

such as computing the disparity maps in real-time. It is the

technique which aims to be fast and edge-aware. It consists

of three steps: constructing a cost volume, fast cost volume

racy of depth map based on the combination of Markov

Random Field (MRF) and sophisticated global optimization

techniques in different researches [10]–[13], but still obtain-

ing a good accuracy in depth estimation remains a chal-

lenge, especially in images with sophisticated or very simple

texture.

Another approach which has been considered to improve

the accuracy of the depth map by mostly preserving the edges

was using the Mutual Information (MI) and SIFT features.

A multisensor synthetic aperture radar (SAR) image registra-

bination of MI, SIFT, plane-fitting and log-chromaticity color

space.

Generally finding a local matching method which performs

well in terms of both speed and accuracy is not easy and

straightforward. But recently employing the random walk

with restart along with optimizing the matching cost proved

that it is possible to have fast matching with pretty accurate

estimation. ARWR is a local matching algorithm based on

random walk with restart method [17] which is used as the

fundamental algorithm in this paper.

At this point it is timely to introduce the field of applica-

sensors (2-4 MP) compared to consumer devices it is impor-

tant to be able to run disparity mapping algorithms at full

with similar colors and missing depth in one depth level.

By solving these challenges a depth map can present correct

and accurate depth information while respecting the structure

of the reference image.

In this paper is presented a method to refine the depth

map generated by the Adaptive Random Walk with Restart

(ARWR) algorithm in order to obtain significant improve-

ments in accuracy. The main features of the proposed method

3- The important point about the proposed filter is that it is

4- When using this filter, the algorithm works better on

WALK WITH RESTART

nical details of the chosen stereo matching method,

ARWR.

H. Javidnia, P. Corcoran: Depth Map Post-Processing Approach