Due to difference of camera parameters, variance of environmental illumination, non-diffuse reflection of object surface, there is always the color discrepancy between stereoscopic image pair in stereo matching, which will decrease the accuracy of disparity computation. To address this problem, an iterative local color correction method is proposed in this paper. First, the Mean shift algorithm is adopted to segment the stereoscopic images with different granularities respectively. Meanwhile, the SIFT features are extracted and used for local region correspondence initially based on object distributions in the images. Then the target image is corrected by using a weighted local color correction function. Because of different view angles of objects in the images, occlusions will occur and cause the initial region correspondence inaccurate. Thus, a stereo matching algorithm is adopted to generate the disparity maps. The region correspondence is then refined based on the dense feature correspondence between the disparity maps, and the weighted local color correction is performed again. The above refinement will be iteratively performed till the disparity result is convergent. Comparing with several color transfer methods on the benchmark image set, the proposed method can improve the color similarity between stereoscopic image pair and improve the accuracy of stereo matching effectively. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
