Objective

2

Stereo matching is an important part of the field of binocular stereo vision. It reconstructs 3D objects or scenes through a pair of 2D images by simulating the visual system of human beings. Stereo matching is widely used in various fields

such as unmanned vehicles

3D noncontact measures

and robot navigation. Most stereo matching algorithms can be divided into two types: global and local stereo matching algorithms. A global algorithm obtains a disparity map by minimizing the energy function; it exhibits the advantage of high matching accuracy. However

a global stereo matching algorithm operates with high computational complexity

and it is difficult to apply to some fields that require programs to act fast. Local matching algorithms use only the neighborhood information of pixels in the window to perform pixel-by-pixel matching

and thus

its matching accuracy is lower than that of global algorithms. Local algorithms have lower computational complexity

expanding the application range of stereo matching. Local stereo matching algorithms generally have four steps: cost computation

cost aggregation

disparity computation

and disparity refinement. In cost computation

the cost value of each pixel in the left and right images is computed by the designed algorithm at all disparity levels. The correlation between the pixel to be matched and the candidate pixel is measured using the cost value; a smaller cost value corresponds to higher relevance. In cost aggregation

a local matching algorithm aggregates the cost value within a matching window by summing

averaging

or using other methods to obtain the cumulative cost value to reduce the impact of outliers. The disparity for each pixel is calculated using local optimization methods and refined using different post-processing methods in the last two steps. However

traditional local stereo matching algorithms cannot fully utilize the edge texture information of images. Thus

such algorithms still exhibit poor performance in matching accuracy in non-occluded regions and regions with disparity discontinuity. A multi-scale stereo matching algorithm based on edge preservation is proposed to meet the real-time requirements for a realistic scene and improve the matching accuracy of an algorithm in non-occluded regions and regions with disparity discontinuity.

Method

2

We use edge detection to obtain the edge matrix of an image. The values in the obtained edge image are filtered

reassigned

and normalized to obtain an edge weight matrix. In a traditional cost computation algorithm

the method of combining the absolute difference with the gradient fully utilizes the pixel relationship among three channels (R

G

and B) of an image. It exhibits limited improvement in regions with disparity discontinuity and can ensure rapid feature of the algorithm. However

higher matching accuracy cannot be guaranteed at edge regions. We fuse the obtained weight matrix with absolute difference and gradient transformation and then set a truncation threshold for the absolute difference and gradient transform algorithms to reduce the influence of the outlier on cost volume

finally forming a new cost computation function. The new cost computation function can provide a smaller cost volume to the pixels in the texture area belonging to the left and right images

and thus

it achieves better discrimination in edge regions. In cost aggregation

edge weight information is combined with the regularization term of a guided image filter to perform aggregation in a cross-scale framework. By changing the fixed regularization term of the guide filter

a larger smoothing factor is superimposed for pixels closer to the edge in the edge texture region of an image

whereas a smaller smoothing factor is superimposed for points farther away from the edge. Therefore

the points closer to the edge acquire a lower cost value. In the disparity computation

we select the point with the smallest cumulative cost value as the corresponding point to obtain the initial disparity map. This map is processed using disparity refinement methods

such as weighted median filter

hole assignment

and left-to-right consistency check

to obtain the final disparity map.

Result

2

We test the algorithm on the Middlebury stereo matching benchmark. Experimental results show that the fusion of texture weight information can more effectively distinguish the cost volume of pixels at the edge region and the number of mismatched pixels at the edge regions of the image is considerably reduced. Moreover

after fusing image information at different scales

the matching accuracy of an image in smooth areas is improved. The average error matching rate of the proposed algorithm is reduced by 3.48% compared with the original algorithm for 21 extended image pairs without any disparity refinement steps. The average error matching rate of the proposed algorithm is 5.77% for four standard image pairs on the Middlebury benchmark

which is better than those of the listed comparison algorithms. Moreover

the error matching rate of the proposed algorithm for venus image pairs in non-occluded regions is 0.18%

and the error matching rate in all the regions is 0.39%. The average peak signal-to-noise ratio of the proposed algorithm on 21 extended image pairs is 20.48 dB. The deviation extent of the pixel disparity of the obtained initial disparity map compared with the real disparity map is the smallest among the listed algorithms. The average running time of the proposed algorithm for 21 extended image pairs is 17.74 s. Compared with the original algorithm

the average running time of the proposed algorithm increases by 0.73 s and still maintains good real-time performance.

Conclusion

2

In this study

we propose a stereo matching algorithm based on edge preservation and an improved guided filter. The proposed stereo matching algorithm effectively improves the matching accuracy of an image in texture regions

further reducing the error matching rate in non-occluded regions and regions with disparity discontinuity.